Ning Zhou

We report a new search of weakly interacting massive particles (WIMPs) using the combined low background data sets in 2016 and 2017 from the PandaX-II experiment in China. The latest data set contains a new exposure of 77.1 live day, with the background reduced to a level of 0.8$\times10^{-3}$ evt/kg/day, improved by a factor of 2.5 in comparison to the previous run in 2016. No excess events were found above the expected background. With a total exposure of 5.4$\times10^4$ kg day, the most stringent upper limit on spin-independent WIMP-nucleon cross section was set for a WIMP with mass larger than 100 GeV/c$^2$, with the lowest exclusion at 8.6$\times10^{-47}$ cm$^2$ at 40 GeV/c$^2$.

We report the weakly interacting massive particle (WIMP) dark matter search results using the first physics-run data of the PandaX-II 500 kg liquid xenon dual-phase time-projection chamber, operating at the China JinPing underground laboratory. No dark matter candidate is identified above background. In combination with the data set during the commissioning run, with a total exposure of 3.3×10^{4} kg day, the most stringent limit to the spin-independent interaction between the ordinary and WIMP dark matter is set for a range of dark matter mass between 5 and 1000 GeV/c^{2}. The best upper limit on the scattering cross section is found 2.5×10^{-46} cm^{2} for the WIMP mass 40 GeV/c^{2} at 90% confidence level.

This document a outlines a set of simplified models for dark matter and its interactions with Standard Model particles. It is intended to summarize the main characteristics that these simplified models have when applied to dark matter searches at the LHC, and to provide a number of useful expressions for reference. The list of models includes both s-channel and t-channel scenarios. For s-channel, spin-0 and spin-1 mediations are discussed, and also realizations where the Higgs particle provides a portal between the dark and visible sectors. The guiding principles underpinning the proposed simplified models are spelled out, and some suggestions for implementation are presented.

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne$\cdot$year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of $3.8\times10^{-47} $cm$^2$ at a dark matter mass of 30 GeV/$c^2$.

Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods.

Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor's angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter's performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. To address this problem, this paper proposes an adaptive filtering approach to adaptively estimate Q and R based on innovation and residual to improve the dynamic state estimation accuracy of the extended Kalman filter (EKF). It is shown through the simulation on the two-area model that the proposed estimation method is more robust against the initial errors in Q and R than the conventional method in estimating the dynamic states of a synchronous machine.

Abstract The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results.

In this paper, the authors use the Numerical algorithm for Subspace State Space System IDentification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals, including low-level pseudo-random noise (LLPRN) and single-mode square wave (SMSW) injected at the Celilo terminal of the Pacific HVDC Intertie (PDCI). An identified model is validated using a cross validation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar operating conditions. The consistent results show that methods in this class can be highly effective, even when the probing signal is small

The frequency and damping of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. The performance properties of three mode-estimation block-processing algorithms from the perspective of near real-time automated stability assessment are demonstrated and examined. The algorithms are: the extended modified Yule Walker (YW); extended modified Yule Walker with spectral analysis (YWS); and sub-space system identification (N4SID). The YW and N4SID have been introduced in previous publications while the YWS is introduced here. Issues addressed include: stability assessment requirements; automated subset selecting identified modes; using algorithms in an automated format; data assumptions and quality; and expected algorithm estimation performance.

This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data.

This paper proposes a robust recursive least square (RRLS) algorithm for online identification of power system modes based on measurement data. The measurement data can be either ambient or ringdown. Also, the mode estimation is provided in real-time. The validity of the proposed RRLS algorithm is demonstrated with both simulation data from a 17-machine model and field measurement data from a wide area measurement system (WAMS). Comparison with the conventional recursive least square (RLS) and least mean square (LMS) algorithms shows that the proposed RRLS algorithm can identify the modes from the combined ringdown and ambient signals with outliers and missing data in real-time without noticeable performance degradation. An adaptive detrend algorithm is also proposed to remove the signal trend based on the RRLS algorithm. It is shown that the algorithm can keep up with the measurement data flow and work online to provide real-time mode estimation.

In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a nonlinear system with non-Gaussian noise. The proposed extended PF improves robustness of the basic PF through iterative sampling and inflation of particle dispersion. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF's performance is evaluated and compared with the basic PF, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). The extended PF results showed high accuracy and robustness against measurement and model noise.

The paper proposes an optimized leader-follower formation control for the multi-agent systems with unknown nonlinear dynamics. Usually, optimal control is designed based on the solution of the Hamilton-Jacobi-Bellman equation, but it is very difficult to solve the equation because of the unknown dynamic and inherent nonlinearity. Specifically, to multi-agent systems, it will become more complicated owing to the state coupling problem in control design. In order to achieve the optimized control, the reinforcement learning algorithm of the identifier-actor-critic architecture is implemented based on fuzzy logic system (FLS) approximators. The identifier is designed for estimating the unknown multi-agent dynamics; the actor and critic FLSs are constructed for executing control behavior and evaluating control performance, respectively. According to Lyapunov stability theory, it is proven that the desired optimizing performance can be arrived. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach.

The PandaX-4T experiment, a 4-ton scale dark matter direct detection experiment, is being planned at the China Jinping Un- derground Laboratory. In this paper we present a simulation study of the expected background in this experiment. In a 2.8-ton fiducial mass and the signal region between 1–10 keV electron equivalent energy, the total electron recoil background is found to be 4:9 × 10−5 kg−1d−1keV−1. The nuclear recoil background in the same region is 2:8 × 10−7 kg−1d−1keV−1. With an exposure of 5.6 ton-years, the sensitivity of PandaX-4T could reach a minimum spin-independent dark matter-nucleon cross section of 6 × 10−48 cm2 at a dark matter mass of 40 GeV/c2.

New constraints are presented on the spin-dependent weakly-interacting-massive-particle–– (WIMP-)nucleon interaction from the PandaX-II experiment, using a data set corresponding to a total exposure of 3.3×104 kg day. Assuming a standard axial-vector spin-dependent WIMP interaction with Xe129 and Xe131 nuclei, the most stringent upper limits on WIMP-neutron cross sections for WIMPs with masses above 10 GeV/c2 are set in all dark matter direct detection experiments. The minimum upper limit of 4.1×10−41 cm2 at 90% confidence level is obtained for a WIMP mass of 40 GeV/c2. This represents more than a factor of 2 improvement on the best available limits at this and higher masses. These improved cross-section limits provide more stringent constraints on the effective WIMP-proton and WIMP-neutron couplings.Received 21 November 2016DOI:https://doi.org/10.1103/PhysRevLett.118.071301© 2017 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasParticle dark matterPhysical SystemsWeakly interacting massive particlesTechniquesDark matter detectorsGravitation, Cosmology & Astrophysics

Summary This paper investigates the control problem of finite‐time attitude synchronization and tracking for a group of rigid spacecraft in the presence of environmental disturbances. A new fast terminal sliding manifold is developed for multiple spacecraft formation flying under the undirected graph topology. On the basis of the finite‐time control and adaptive control strategies, two novel decentralized finite‐time control laws are proposed to force the spacecraft attitude error dynamics to converge to small regions in finite time, and adaptive control is applied to reject the disturbance. The finite‐time convergence and stability of the closed‐loop system can be guaranteed by Lyapunov theory. Simulation examples are provided to illustrate the feasibility of the control algorithm. Copyright © 2014 John Wiley &amp; Sons, Ltd.

The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider (CEPC) is one of such proposed Higgs factories. The CEPC is an e+e− circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100 km in circumference, it will operate at a center-of-mass energy of 240 GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.

We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2.7\times 10^4$ kg$\cdot$day. No solar axion or galactic axion-like dark matter particle candidate has been identified. The upper limit on the axion-electron coupling ($g_{Ae}$) from the solar flux is found to be about $4.35 \times 10^{-12}$ in mass range from $10^{-5}$ to 1 keV/$c^2$ with 90\% confidence level, similar to the recent LUX result. We also report a new best limit from the $^{57}$Fe de-excitation. On the other hand, the upper limit from the galactic axions is on the order of $10^{-13}$ in the mass range from 1 keV/$c^2$ to 10 keV/$c^2$ with 90\% confidence level, slightly improved compared with the LUX.

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$.

ABSTRACT Objectives To investigate the efficiency and movement pattern of upper arch expansion using Invisalign aligners. The correlation between the amount of designed expansion and the efficiency of bodily expansion was evaluated, as were the initial molar torque and efficiency of bodily expansion. Materials and Methods Twenty Chinese adult patients who underwent arch expansion with Invisalign aligners were included in this study. Records of pretreatment (T0 stage) and immediately after completing the expansion phase (T1 stage) were collected, including digital models and cone-beam computed tomography. Dolphin 3D, Geomagic Studio 12.0, and Meazure software were employed to measure data and calculate differences between the expected and actual outcomes. Results There were significant differences between the expected and actual expansion amounts (P&amp;lt; .05). The average expansion efficiencies of the upper canine crown, first premolar crown, second premolar crown, and first molar crown were 79.75 ± 15.23%, 76.1 ± 18.32%, 73.27 ± 19.91%, and 68.31 ± 24.41%, respectively. The average efficiency of bodily expansion movement for the maxillary first molar was 36.35 ± 29.32%. Negative correlations were found between preset expansion amounts and the efficiency of bodily expansion movement (P &amp;lt; .05), and between initial maxillary first molar torque and efficiency of bodily expansion movement (P &amp;lt; .05). Conclusions Aligners could increase the arch width, but expansion was achieved by tipping movement. The evaluation of initial position and preset of sufficient root-buccal torque of posterior teeth were necessary due to the lower efficiency of bodily buccal expansion by the Invisalign system.

We present PandaX-II constraints on candidate WIMP-nucleon effective interactions involving the nucleon or WIMP spin, including, in addition to standard axial spin-dependent (SD) scattering, various couplings among vector and axial currents, magnetic and electric dipole moments, and tensor interactions. The data set corresponding to a total exposure of 54-ton-days is reanalyzed to determine constraints as a function of the WIMP mass and isospin coupling. We obtain WIMP-nucleon cross section bounds of $\rm 1.6 \times 10^{-41} cm^2$ and $\rm 9.0 \times 10^{-42} cm^2$ ($90\%$ c.l.) for neutron-only SD and tensor coupling, respectively, for a mass $M_\mathrm{WIMP} \sim {\rm 40~GeV}/c^2$. The SD limits are the best currently available for $M_\mathrm{WIMP} > {\rm 40~GeV}/c^2$. We show that PandaX-II has reached a sensitivity sufficient to probe a variety of other candidate spin-dependent interactions at the weak scale.

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$.

We report a novel search for the cosmic-ray boosted dark matter using the 100 tonne·day full dataset of the PandaX-II detector located at the China Jinping Underground Laboratory. With the extra energy gained from the cosmic rays, sub-GeV dark matter particles can produce visible recoil signals in the detector. The diurnal modulations in rate and energy spectrum are utilized to further enhance the signal sensitivity. Our result excludes the dark matter-nucleon elastic scattering cross section between 10^{-31} and 10^{-28} cm^{2} for dark matter masses from 0.1 MeV/c^{2} to 0.1 GeV/c^{2}, with a large parameter space previously unexplored by experimental collaborations.

Fibrous porous ceramics are attractive for use as thermal insulation materials. However, the intrinsic brittleness of rigid materials has remained challenging and severely restricts their applications. Here, we demonstrated a facile method for fabricating elastic fibrous porous ceramics (EFPCs) with high compressive strength and low thermal conductivity through ordinary press filtration and subsequent heat treatment. The quasi-layered structure and the well-bonded bridging fibers between layers are the key points for the elasticity of EFPCs. The advanced EFPCs exhibited low density (∼0.126 g cm−3), high compressive stress (∼0.356 MPa), and low thermal conductivity (∼0.026 W m−1 K−1). Compared with rigid porous fibrous materials, the EFPCs had deformability and excellent shape recovery. In contrast to flexible materials, the EFPCs possessed high compressive stress, thus endowing them with good resistance to deformation. The emergence of this fascinating material may provide new insights for candidate materials in thermal insulation and other fields.

A search for interactions from solar 8B neutrinos elastically scattering off xenon nuclei using PandaX-4T commissioning data is reported. The energy threshold of this search is further lowered compared with the previous search for dark matter, with various techniques utilized to suppress the background that emerges from data with the lowered threshold. A blind analysis is performed on the data with an effective exposure of 0.48 tonne year, and no significant excess of events is observed. Among the results obtained using the neutrino-nucleus coherent scattering, our results give the best constraint on the solar 8B neutrino flux. We further provide a more stringent limit on the cross section between dark matter and nucleon in the mass range from 3 to 9 GeV/c2.Received 7 July 2022Accepted 23 November 2022DOI:https://doi.org/10.1103/PhysRevLett.130.021802Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDark matterParticle dark matterSolar neutrinosParticles & FieldsGravitation, Cosmology & Astrophysics

Prony analysis has been applied to estimate inter-area oscillation modes using phasor measurement unit (PMU) measurements. To suppress noise and signal offset effects, a high-order Prony model usually is used to over-fit the data. As such, some trivial modes are intentionally added to improve the estimation accuracy of the dominant modes. Therefore, to reduce the rate of false alarms, it is important to distinguish between the dominant modes that reflect the dynamic features of a power system and the trivial modes that are artificially introduced to improve the estimation accuracy. In this paper, a stepwise-regression method is applied to automatically identify the dominant modes from Prony analysis. A Monte Carlo method is applied to evaluate the performance of the proposed method using data obtained from simulations. Field-measured PMU data are used to verify the applicability of the proposed method. A comparison of results obtained using the proposed approach with results from a traditional energy-sorting method shows the improved performance of the proposed method.

Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper.

This paper provides a comparative review of five existing ambient electromechanical mode shape estimation algorithms, i.e., the Transfer Function (TF), Spectral, Frequency Domain Decomposition (FDD), Channel Matching, and Subspace Methods. It is also shown that the TF Method is a general approach to estimating mode shape and that the Spectral, FDD, and Channel Matching Methods are actually special cases of it. Additionally, some of the variations of the Subspace Method are reviewed and the Numerical algorithm for Subspace State Space System IDentification (N4SID) is implemented. The five algorithms are then compared using data simulated from a 17-machine model of the Western Electricity Coordinating Council (WECC) under ambient conditions with both low and high damping, as well as during the case where ambient data is disrupted by an oscillatory ringdown. The performance of the algorithms is compared using the statistics from Monte Carlo simulations and results from measured WECC data, and a discussion of the practical issues surrounding their implementation, including cases where power system probing is an option, is provided. The paper concludes with some recommendations as to the appropriate use of the various techniques.

We search for nuclear recoil signals of dark matter models with a light mediator in PandaX-II, a direct detection experiment in the China Jinping underground laboratory. Using data collected in 2016 and 2017 runs, corresponding to a total exposure of 54 ton day, we set upper limits on the zero-momentum dark matter-nucleon cross section. These limits have a strong dependence on the mediator mass when it is comparable to or below the typical momentum transfer. We apply our results to constrain self-interacting dark matter models with a light mediator mixing with standard model particles, and set strong limits on the model parameter space for the dark matter mass ranging from 5 GeV to 10 TeV.

This paper develops a self-coherence method for detecting sustained oscillations using phasor measurement unit (PMU) data. Sustained oscillations decrease system performance and introduce potential reliability issues. Timely detection of the oscillations at an early stage provides the opportunity for taking remedial reaction. Using high-speed time-synchronized PMU data, this paper details a self-coherence method for detecting sustained oscillation, even when the oscillation amplitude is lower than ambient noise. Simulation and field measurement data are used to evaluate the proposed method's performance. It is shown that the proposed method can detect sustained oscillations and estimate oscillation frequencies with a low signal-to-noise ratio. Comparison with a power spectral density method also shows that the proposed self-coherence method performs better.

This article tackles the trajectory tracking problem for a non-holonomic wheeled mobile robot (WMR) with non-random and random disturbances. A nonlinear disturbance observer with extended Kalman filter (NDEKF) is designed to observe the velocity and the non-random disturbance of the WMR. An error feedback controller and a kinematic controller are proposed to achieve the disturbance compensation and perfect position tracking. The mean square exponential boundedness for the estimation error of NDEKF is presented. Applying the Lyapunov stability theory, it is proved that the velocity and position tracking error of the double closed-loop system are uniformly ultimately asymptotically stable. Finally, numerical simulations demonstrate the validity of the presented methodology.

In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples.

Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role in unit commitment, economic dispatch, and storage system management. However, forecasting solar PV power in high temporal resolution such as five-minute resolution is challenging because most of PV forecasting models can only achieve the same temporal resolution as their predictors(i.e., weather variables), whose temporal resolution is usually low (i.e., hourly). To address this challenge, similarity-based forecasting models (SBFMs) are advocated in this paper to forecast PV power in high temporal resolution using low temporal resolution weather variables. To effectively generalize the model for different scenarios of available weather data, three forecasting models (i.e., basic SBFM, categorical SBFM, and hierarchical SBFM) are proposed. As a case study, the PV power generated by the solar panels on the rooftop of a commercial building is forecasted for the next day with a five-minute resolution under three different scenarios of available weather data. The leave-one-out cross-validation analysis reveals that using only two or three weather variables, the proposed SBFMs can achieve higher forecasting accuracy than several benchmark models.

We point out a new type of diurnal effect for the cosmic ray boosted dark matter (DM). The DM-nucleon interactions not only allow the direct detection of DM with nuclear recoils, but also allow cosmic rays to scatter with and boost the nonrelativistic DM to higher energies. If the DM-nuclei scattering cross sections are sufficiently large, the DM flux is attenuated as it propagates through the Earth, leading to a strong diurnal modulation. This diurnal modulation provides another prominent signature for the direct detection of boosted sub-GeV DM, in addition to signals with higher recoil energy.

This article investigates the attitude stabilization problem of a rigid spacecraft with actuator saturation and failures. Two neural network-based control schemes are proposed using anti-saturation adaptive strategies. To satisfy the input constraint, we design two controllers in a saturation function structure. Taking into account the modeling uncertainties, external disturbances, and adverse effects from actuator faults and failures, the first anti-saturation adaptive controller is implemented based on radial basis function neural networks (RBFNNs) with a fixed-time terminal sliding mode (FTTSM) containing a tunable parameter. Then, we upgrade the proposed controller to a fully adaptive-gain anti-saturation version, in order to strengthen the robustness and adaptivity with respect to actuator faults and failures, unknown mass properties, and external disturbances. In the two schemes, all of the designed adaptive parameters are scalars, thus they only require light computational load and can avoid the redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed methods is illustrated via two numerical examples.

Recent advances in the Medical Internet of Things (MIoT) and big data enable a prospering platform for pervasive healthcare and facilitate the transformation from hospital-centered to human-centered healthcare. Wearable devices as human interfaces provide first-hand data and real-time monitoring, which are key technologies in the MIoT. Several remarkable surveys have been conducted to summarize the recent progress in wearable sensors and systems for the MIoT and pervasive medicine. However, few have focused on wearable optical sensing (WOS) technologies, which is an emerging sensing modality in wearable devices. WOS can achieve high precision, high compatibility, high anti-interference, and low motion artifacts for human vital signal acquisition, which are particularly useful in special scenarios such as intensive care units (ICUs). These technologies can also be integrated with smart fabrics or mobile computing for out-of-hospital healthcare. This work provides the first literature review of WOS for pervasive medicine. We aim to systematically summarize the emerging WOS technologies in the MIoT for disease diagnosis and health monitoring. Specifically, this review covers the technical bases and design principles of major WOS technologies and their application domains for monitoring and treatment. We also discuss the opportunities and challenges, especially in the COVID-19 outbreak.

We report a search on sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne year exposure collected by the PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and the electron. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such a dark matter candidate into photon final states. In particular, we present the first direct detection limits for a vector (axial-vector) interaction which are the strongest in the mass range from 35 to 55 (25 to 45) keV/c^{2} in comparison to other astrophysical and cosmological constraints.

Silver nanoparticles (AgNPs) are widely used due to their unique antibacterial properties and excellent photoelectric properties.

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal “basis” of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.

IET Renewable Power GenerationVolume 11, Issue 10 p. 1274-1280 Special Issue: Performance Assessment and Condition Monitoring of Photovoltaic Systems for Improved Energy YieldFree Access Weather forecasting error in solar energy forecasting Hossein Sangrody, Corresponding Author Hossein Sangrody habdoll1@binghamton.edu Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorMorteza Sarailoo, Morteza Sarailoo Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorNing Zhou, Ning Zhou Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorNhu Tran, Nhu Tran School of Management, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorMahdi Motalleb, Mahdi Motalleb Department of Electrical Engineering, University of Hawaii, Manoa, HI, USASearch for more papers by this authorElham Foruzan, Elham Foruzan Department of Electrical and Computer Engineering, University of Nebraska Lincoln, Lincoln, NE, USASearch for more papers by this author Hossein Sangrody, Corresponding Author Hossein Sangrody habdoll1@binghamton.edu Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorMorteza Sarailoo, Morteza Sarailoo Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorNing Zhou, Ning Zhou Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorNhu Tran, Nhu Tran School of Management, Binghamton University, State University of New York, Binghamton, USASearch for more papers by this authorMahdi Motalleb, Mahdi Motalleb Department of Electrical Engineering, University of Hawaii, Manoa, HI, USASearch for more papers by this authorElham Foruzan, Elham Foruzan Department of Electrical and Computer Engineering, University of Nebraska Lincoln, Lincoln, NE, USASearch for more papers by this author First published: 11 July 2017 https://doi.org/10.1049/iet-rpg.2016.1043Citations: 36AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. A study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for 6 days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting. 1 Introduction Both energy and load forecasting play a critical role in planning, control, and operation of power systems. As renewable energy resources are penetrating the power grid at an accelerating speed, their indispatchability, variability, and uncertainty have presented unprecedented challenges to power grid operations and planning. As a result, accurate forecasting is more vital than before [1-3]. Most of energy forecasting models are trained using observed weather variables and there are lots of studies which focused on improving their forecasting models with efficient methodologies. However, the trained models are applied using forecasted weather variables [4]. The issue of this practice is that if uncorrelated weather variables or forecasted weather variables with huge errors are entered in the forecasting model, the resulting forecast may not be satisfactory. These errors are more severe when forecast lead time gets longer. Consequently, the efficiency of a forecasting model is unacceptable when the inputs suffer from large errors or include uncorrelated data. Many studies have been carried out to forecast renewable distributed energy resources' (DERs) generation and many methods have been suggested to improve forecasting models [5-7]. In [8], an intelligent method is proposed to forecast wind speed and solar radiation based on predictive coding and image processing. In [9], authors provided a survey on using ensemble methods for wind speed/power forecasting and solar irradiance forecasting. They concluded that generally the ensemble forecasting methods surpass other non-ensemble methods. A comprehensive survey study on the latest state of the art in solar energy forecasting was conducted in [10]. This paper discusses motivations, effects of forecast horizon, benefits of regional forecast, origin of inputs, and advantages of probabilistic forecast over deterministic forecast. The output errors of hybrid photovoltaic (PV) power forecasting models were studied in [11]. The authors discussed least square support vector machines, artificial neural network (ANN), and hybrid statistical model based on least square support vector machines with wavelet decomposition. They used conventional metrics, such as the root-mean-square error (RMSE), mean bias error, and mean absolute error (MAE), to evaluate the performance of the different methods. In [12], authors proposed a new method for online forecasting of the output power of PV systems. The proposed approach consists of two stages. In first stage, normalised statistical solar powers of a clear-sky model are acquired. In second stage, an adaptive linear time series approach is utilised to forecast the power output. A novel hybrid algorithm was proposed in [13] to forecast output power of a PV. The proposed algorithm is based on the combination of least square support vector machine and group method of data handling. The performance of the proposed algorithm was compared with those two methods, using different strategies (direct, recursive and DirRec). The results showed proposed algorithm with DirRec strategy has a significant improvement over those two methods and traditional ANN. The effects of the aerosol data, water vapour data and ozone content data on the output of the clear-sky models were studied for estimation of clear-sky solar irradiance [14]. In this study, the performance of three clear-sky solar irradiance models, namely European Solar Radiation Atlas clear-sky model, simplified solar irradiance scheme clear-sky model, and Reference Evaluation on Solar Transmittance 2 clear-sky model, was evaluated and compared. In addition, they studied the performance of those models using the same atmospheric input data but at different elevation. In [15], the effect of uncertainty in temperature is considered in load forecasting; however, this work was only on energy demand and the only weather variable in this study was temperature. Chen et al. [16] proposed an artificial intelligent-based technique for forecasting solar power which required the past power measurement, solar irradiance forecast, humidity and temperature as inputs. These studies have laid a solid ground for forecasting the generations of renewable DERs and at the same time revealed the needs of considering the impact of weather forecasting errors on the forecasted power outputs. Fig. 1 shows a typical process for the solar PV generation forecasting using weather data and historical generation data. Fig. 1Open in figure viewerPowerPoint Typical solar PV generation forecasting process As shown, the forecasting model is trained using observed weather data and historical data of solar PV generation. After the model is well trained using ANN, the forecasted weather data will be used to have solar PV generation forecast. The major contribution of this study is to analysis the uncertainty of weather forecast and its effect on the solar PV generation forecast. In this study, the impact of weather forecast errors for several weather variables including sky cover, dew point, relative humidity, temperature, and wind on the performance of solar PV generation forecasting is assessed. As a case study, the solar energy generated by the solar PV panels on the rooftop of Engineering and Science buildings at Binghamton University is considered. Accordingly, the most commonly used weather variables in solar forecasting such a sky cover, dew point, relative humidity, temperature, and wind are considered for assessment. The aforementioned variables for both observed and forecasted values of 6 days ahead are collected. The errors in the forecasted weather variables of each day are evaluated by statistical metrics. Using the bootstrapping method, the uncertainty of the forecasting errors is quantified. Then, by applying correlation analysis, the influential variables are identified and selected for forecasting. Finally, by applying the forecasted weather variables along with the observed data, the performance of forecasting models in dealing with inputs errors is studied. This paper is organised as follows: In Section 2, observed and forecasted weather data and their classification are discussed. Section 3 elaborates the weather data error analysis and quantifies errors in forecasted variables. Simulation results are presented in Section 4 where the influential weather variables are identified using a correlation method and the performance of the energy forecasting method with observed and forecasted weather data is evaluated. At the end, the conclusions are drawn in Section 5. 2 Observed and forecasted weather data acquisition To assess the effects of weather variables in forecasting model, weather data are extracted from National Oceanic and Atmospheric Administration (NOAA), which provides data in public domain. Both forecasted and observed data of weather variables are available for most of local areas in USA with hourly resolution. The observed weather variables are available online for 4 days ahead and most of them are update hourly while the forecasted weather variables are updated hourly for 6 days ahead. The most influential driving variables in solar PV generation forecasting model are time, date, and weather variables [10, 17]. For selecting predictors in solar energy forecasting model, sky cover, relative humidity, dew point, temperature, wind speed, pressure, and precipitation are usually considered as weather variables. The observed weather variables provided by the NOAA include weather condition, sky cover, dew point, relative humidity, visibility, pressure, temperature, precipitation, and wind speed. However, the forecasted weather variables provided by the NOAA are sky cover, dew point, relative humidity, participation potential, relative humidity, temperature, and wind. Among observed and forecasted categories, sky cover, dew point, relative humidity, temperature, and wind speed are common in both categories and also represented in hourly interval. Accordingly, the aforementioned variables are considered for weather data analysis and selecting predictors in the following sections. However, the sky cover, which is one of the most important driving inputs in solar PV generation forecasting, is represented differently for the observed and forecasted data. In the observed weather data, the sky cover is represented by categories as depicted in Table 1 while in the forecasted data, it is represented by numerical percentage. To compare the observed and forecasted data of this weather variable, both data types are categorised in a common category. The fourth column of Table 1 gives a numerical percentage category for the observed data. Similarly, the forecasted sky cover data is also classified within five groups shown in the fourth column of Table 1. Table 1. Numerical classification for observed and forecasted sky cover Sky condition Opaque cloud coverage Opaque cloud coverage, % Percentage category, % clear 1/8 and less sky cover < 12.5 0 mostly clear 1/8–3/8 12.5 ≤ sky cover < 37.5 25 partly cloudy 3/8–5/8 37.5 ≤ sky cover < 62.5 50 mostly cloudy 5/8–7/8 62.5 ≤ sky cover < 87.5 75 cloudy 7/8–8/8 87.5 ≤ sky cover 100 3 Weather data analysis As mentioned, the data provided by the NOAA for the historical forecasted data spans for 6 days ahead. To assess the error corresponding to weather forecasting, the observed weather data are compared with historical forecasted weather data for a complete year during 20 May 2016 to the end of the day on 19 May 2017 with error metrics. To quantify errors, there are different commonly used metrics such as the mean absolute percentage error (MAPE), MAE, mean-squared error, and RMSE [18]. In this study, the MAPE defined by (1) is used to evaluate the error in solar PV generation forecasting results. However, since some weather indicators are zero, the MAE defined by (2) is used to represent the error in weather forecasting (1) (2) where N is the number of observations, is the actual target value at time instant i, the symbol is the input vector, and f is the forecasting model. In addition, to estimate the error statistics, the bootstrapping method is applied [19]. The bootstrapping is an efficient numerical approach for estimating some statistical parameters like mean and standard deviation of population from a sample. Bootstrapping, which is based on resampling and replacement of a sample, does not make any assumptions about the distribution of the sample data. However, it requires that the sample data and its size should be sufficient to well represent the population distribution. In addition, bootstrapping can be used to derive the uncertainty of the estimated statistical parameters. Such uncertainty represented by confidence intervals (CIs) claims to cover the true statistics of population within the intervals with a specified probability. For example, in [20], such a specified probability is 95% which means that the true value of population is located in CIs with the probability of 0.95. For this case, the number of bootstrap resampling cycles and the probability of the CIs are 2500 and 95%, respectively [21]. In this study, the forecast errors, which are the difference between observed and forecasted weather data, are calculated using MAEs in (2) for each hour of six successive days. In addition, to consider the likely direction of forecasting error, bias in error defined by (3) is also considered in the analysis. The sign of the bias represents the direction of the errors, where positive bias indicates the observed data is more than the forecasted value and vice versa (3) where is the forecasted variable which in this case is the historical forecasted weather variables and is the observed weather variables. In addition, in the calculation of MAE in (2), is also used for and observed weather variables are used for . To imply the population statistics of the aforementioned error metrics, bootstrap method is applied. Table 2 depicts the results of calculation for each weather variable in each day of forecasting. On the first row, each day is shown as D #no. Table 2. Statistics of error in weather forecasting for 6 days ahead Type Statistics D #1 D #2 D #3 D #4 D #5 D #6 SC bias −6.46 −5.49 −.4.49 −.221 −2.2 −2.1 MAE 24.27 25.74 28.44 31.32 33.8 35.57 DP bias −2.22 −2.56 −2.8 −2.7 −2.73 −2.68 MAE 2.93 3.44 3.8 4.13 4.7 5.32 RH bias 0.5 −0.34 −0.9 −0.82 −0.92 −0.9 MAE 8.88 9.88 10.64 11.11 11.83 12.27 T bias −2.1 −2.06 −2.07 −2 −1.94 −1.88 MAE 3.08 3.26 3.63 3.95 4.46 4.97 W bias 1.74 2.07 2.67 3.35 3.4 3.25 MAE 3.09 3.44 3.78 4.22 4.36 4.37 SC, sky cover; DP, dew point; RH, relative humidity; T, temperature, W, wind. Table 2 shows that the bias takes negative values for almost all of the forecasted days except for wind which is totally vice versa. This result indicates that the NOAA generally forecasts a value more than real observed variables for sky cover, dew point, relative humidity, and temperature, whereas for the wind, the NOAA provides underestimated values. Such biases in weather forecasting may result in overestimation and underestimation in energy forecasting. In addition, the results of bias indicate that the forecasting residuals in NOAA forecasting model do not have a zero mean. When the residuals of a forecasting model have a mean other than zero, the forecasting model is biased and it can be improved to have better results [22]. The results of autocorrelation function (ACF) also show inefficacy of the weather forecast model. As an example, Fig. 2 shows the ACF of the sky cover forecast error for 1 day ahead with the lag length of 100. As it is illustrated, there are >5% of the spikes out of the bounds. The results of ACFs for other weather variables also show similar results. Therefore, the weather forecasting model in NOAA violates that assumption of no autocorrelation in the residuals which means there is more information left over which can be implemented to improve the results of forecasting. Fig. 2Open in figure viewerPowerPoint ACF of forecasted sky cover for 1 day ahead In Table 3, the results of 95% CIs for the MAE of the errors is depicted. The two numbers in the table cells are the lower and upper bounds, respectively. Table 3. 95% CIs of MAE in forecasting of weather variables (lower bound, upper bound) Type D #1 D #2 D #3 D #4 D #5 D #6 SC 22.6, 25.9 24.1, 27.37 26.9, 29.95 29.87, 32.77 32.36, 35.22 34, 37 DP 2.7, 3.1 3.23, 3.64 3.57, 4.03 3.86, 4.38 4.4, 5 5, 5.64 RH 8.4, 9.4 9.3, 10.4 10, 11.2 10.45, 11.8 11.15, 12.52 11.6, 12.95 T 2.9, 3.3 3.1, 3.4 3.4, 3.85 3.7, 4.1 4.2, 4.7 4.6, 5.3 W 2.93, 3.26 3.26, 3.62 3.59, 3.98 4.02, 4.43 4.14, 4.59 4.14, 4.6 Another representation of the MAE in Table 2 and CIs in Table 3 is illustrated by Figs. 3 and 4 where Fig. 3 represents MAE of sky cover and similarly Fig. 4 illustrated MAEs in other weather variables. Fig. 3Open in figure viewerPowerPoint MAE of forecasted sky cover for 6 days ahead Fig. 4Open in figure viewerPowerPoint MAEs of forecasted variables 6 days ahead (a) MAE of forecasted dew point for 6 days ahead, (b) MAE of forecasted temperature for 6 days ahead, (c) MAE of forecasted relative humidity for 6 days ahead, (d) MAE of forecasted wind speed for six days ahead 4 Simulation results To study the effect of observed and historical weather data on solar PV energy generation, the solar PV generation by the solar panels installed on the rooftop of the Engineering and Science building at the State University of New York at Binghamton is considered as case study. For this purpose, the observed weather data and corresponding 6 days ahead forecasted weather data for the likely influential weather variables on energy forecasting and at the same location of solar panels are considered during one study year from 20 May 2016 to end of the day on 19 May 2017. In addition, the solar energy generated by the panels is acquired by hourly resolution during the studying time. Fig. 5 illustrates the daily solar energy for the case study during studying year. The solar panels of the case study are capable of providing maximum nominal energy of 120 kWh. Fig. 5Open in figure viewerPowerPoint 24-hour profile of solar PV generation for the case study For the purpose of forecasting analysis, the peak energy is selected and both observed and forecasted weather variables corresponding to the peak time of solar generation are extracted from weather data sets. Thus, from this point on, solar energy refers to the daily peak solar energy generation and weather data refers to the corresponding data at daily peak time of solar energy generation. In most cases, weather variables along with other indicators like time and date are used as predictor variables in solar energy forecasting models. As mentioned in second section, the available variables in both observed and forecasted weather data provided by the NOAA are sky cover, dew point, relative humidity, temperature, and wind speed. However, the existence unrelated variables as predictors in training model may lead to inaccurate results. In addition, the correlation between predictors may lead to misleading results. Thus, a predictors selection analysis is conducted before applying the candidate predictors in forecasting model. Note that the weather data applied in the training process of energy forecasting are observed data and here in this case also observed weather variables are applied in predictors analysis. Fig. 6 shows the correlation analysis between solar energy and five weather variables. As shown in this table, the correlation coefficients between energy and five weather variables indicate that the dew point and wind speed have very low correlations with energy. On the other hand, relative humidity has the highest correlation with energy with the value of −0.61. In addition, dew point and temperature are highly correlated (r = 0.9). Fig. 7 illustrates the scatter plots of these four variable of energy-dew point, energy-wind, energy-relative humidity, and dew point temperature. As shown in Figs. 7a and b, the scatter plots of energy with dew point and wind have sporadic patterns while as illustrated in Fig. 7c, energy and relative humidity are highly correlated. In addition, the scatter plot of dew point and temperature, shown in Fig. 7d, indicates high correlation between these two predictors. Thus, considering the results of correlation analysis, dew point and wind are excluded from the predictors set and three predictors of sky cover, relative humidity, and wind are selected for training the solar PV energy forecasting. Note that the authors also considered all states of possible combination of predictors (31 states) in training and validation of forecasting model using ANN and the results also confirmed the optimal selection of predictors as sky cover, relative humidity, and temperature. Fig. 6Open in figure viewerPowerPoint Results of correlation between energy and weather variables Fig. 7Open in figure viewerPowerPoint Correlation between solar energy and weather variables (a) Correlation between energy and dew point, (b) Correlation between energy and wind, (c) Correlation between energy and relative humidity, (d) Correlation between temperature and dew point As mentioned before, there are many methods and models for training and modelling for solar energy forecasting. However, since the purpose of this study is more about the analysis of weather variables in solar PV energy forecasting and the effect of their uncertainties, only one of the forecasting methods is chosen for the simulation. Among lot of the forecasting methods, ANN method, is one of the most commonly used and efficient methods and it is applied for this case [23]. The ANN method is like a black-box model, which provides an efficient way to model a complex non-linear system. To model a system using an ANN model, there is no need to figure out the closed-form equations of the system or to know the complex relationship between input and output variables. The neural network used in this study is a feed forward supervised learning model with one hidden layer. The number of hidden neurons in the hidden layer is three [24]. In addition, the Levenberg-Marquardt algorithm is used for model training. The solar PV generation forecasting model sets the daily peak value of solar energy as the dependent variable and three selected weather indicators of sky cover, relative humidity, temperature at the corresponding peak time as independent variables. First, the model is trained with solar energy and observed weather data. Then, the historical forecasted weather data for 6 days ahead are applied to the trained model. Accordingly, there are six forecasted solar PV energy sets corresponding to the 6 days of forecasted weather data as inputs. The forecasted energy for 6 days ahead is compared with the real generated solar energy and corresponding errors for each day are represented by MAPE and MAE. Fig. 8 depicts the results of errors for the observed and historical forecasted weather data. The blue bars show the errors corresponding to the MAPEs of forecasted energy using historical forecasted data and the red bar is corresponding to the MAPE of forecasted energy using observed weather data. Based on the increasing error trend, a huge proportion of error belongs to the observed data. However, in the forecasting with historical forecasted data (which carry significant input errors) the error is not significant in compared with the observed data (which is considered as actual weather data with likely small error). Table 4 also depicts the result of MAE for the forecast errors using observed and historical forecasting data. Table 4. Results of applying forecasted weather variables in solar energy model Statistics D #1 D #2 D #3 D #4 D #5 D #6 MAE 10.29 10.74 11.71 11.98 13 12.1 Fig. 8Open in figure viewerPowerPoint MAPEs results using observed and historical forecasted data According to Fig. 8 and Table 4, the ANN model is robust enough to handle the input errors. Thus, the forecast weather variables can be handled with a well-designed forecasting model although they may carry considerable errors. This result can help a network manager to efficiently estimate DERs hosting capacity considering by defining an acceptable errors dictated by forecasted weather data. Although energy forecasting model can handle the error in forecasting weather variables, such an error can be decreased if the influential weather variables are identified and weather forecaster improves the accuracy of those influential variables, specifically. To achieve this purpose, a sensitivity test is conducted in which three scenarios for the three weather variables inputs (sky cover, relative humidity, and temperature) of energy forecasting model are implemented. In each scenario, it is supposed that for one of the weather variables, the actual weather data is available for the 6 days ahead instead of forecasted data while for other weather variables, historical forecasted data are applied in energy forecasting model. Accordingly, in each scenario, the observed data for one of weather variables along with historical data for other weather variables are applied as inputs of the energy forecasting model and the MAPEs of all scenarios are compared with the MAPE of forecasting using only forecasted weather variables. The result of sensitivity test for all three scenarios along with forecasting using only forecasted weather variables is shown in Fig. 9. In this figure, the MAPE illustrated by continuous black line is the error in energy forecasting model using only historical forecasted weather variables. Fig. 9Open in figure viewerPowerPoint Sensitivity analysis of weather variables As seen in Fig. 9, the green line which belongs to the scenario test of relative humidity has the least MAPE for the energy forecasting. In this scenario, only for the relative humidity, the observed data of 6 days ahead are applied in energy forecasting model while other weather variables are retained as forecasted values. Accordingly, if the forecaster can improve the accuracy of this weather variable by using accurate measurement and/or weather forecasting model, the energy forecaster will be able to provide better forecasting results. In addition, the blue line, which represents sky cover variable, also can improve the results of energy forecasting if the forecasted value of this variable is provided accurately. 5 Conclusion In this paper, the uncertainty in weather forecasting was studied for solar PV generation forecasting. For this purpose, the data for both observed and 6 days forecasted values of weather variables were extracted from NOAA for a complete year of studying time. The common variables between both observed and forecasted variables were selected as potential influential predictors for solar energy forecasting model. Then the errors in weather forecasting were derived by comparing the observed and 6 days forecasted values. Using the bootstrapping method, the errors corresponding to each 6 days of forecasting were presented by statistical metrics. The results of error analysis indicate bias and overestimating in weather forecasting for all weather variables except wind speed which is underestimated for all 6 days of weather forecasting. Using correlation analysis, the most influential variables, i.e. sky cover, relative humidity, and temperature were selected for energy forecasting model training. The impact of forecasted weather data errors on the forecasting model was assessed using the ANN model. The MAPEs results show that although there are significant errors in historical forecasted data, the forecasting model can handle the errors, decently. Finally, a sensitivity test on weather variable was performed to identify weather variables whose accurate values can significantly improve the energy forecasting. 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Two finite-time control algorithms are developed for distributed cooperative attitude synchronization of multiple spacecraft with a dynamic virtual leader. Each spacecraft is modeled as a rigid body incorporating with model uncertainty and unknown external disturbance. The virtual leader gives commands to some of the follower spacecraft, and the communication network between followers can be an undirected or a directed graph. By using two neighborhood synchronization error signals, a finite-time control algorithm is designed associated with adaptive mechanism such that all follower spacecraft synchronize to the virtual leader in finite time. Then, a novel estimator-based finite-time distributed cooperative control algorithm is developed by using the followers estimates of the virtual leader, and the convergence of the attitude and angular velocity errors can be guaranteed in finite time. Moreover, both of the control strategies are chattering-free for their continuous design. Simulation examples are illustrated to demonstrate the validity of the two algorithms.

This paper presents a general approach for coherency detection in bulk power systems using the projection pursuit (PP) theory. Supported by the concept of center of inertia (COI) in power systems, the PP theory is employed to model the wide-area coherency detection as an optimization problem. In the proposed method, the optimal projection direction in high dimensional orthogonal space is explored in order to detect the coherent groups via the data from synchronous phasor measurement units (PMUs). Two quantitative indices constructed with projection assessment index (PI), the objective of the optimization model, are then defined in order to determine the critical coherent group and the dominant coherent groups. The coherency detection criterion and the implementation framework for the proposed approach are also presented. Simulation data from the 16-machine 68-bus test system and China Southern power Grid (CSG), along with actual field-measurement data retrieved from WAMS database in the CSG, are employed to demonstrate the effectiveness and applicability of the proposed algorithm under different disturbances. It is shown that the proposed methodology successfully detects the dominant coherent groups of generators and buses in bulk power system via the wide-area field-measurement data.

Accurate estimation of dynamic states (such as synchronous machine rotor angle and speed) is critical for monitoring and controlling transient stability. Extended Kalman filter (EKF)-based approaches have been developed for estimating dynamic states. In order to improve the EKF's performance in estimating dynamic states of a synchronous machine, this paper proposes a multi-step adaptive interpolation (MSAI) approach to achieve a balance between estimation accuracy and computational time. This approach consists of three major steps. First, two indexes are calculated to quantify the nonlinearity of the state transition function and measurement function, respectively. Second, based on the nonlinearity indexes, the interpolation factor is determined using a finite state machine model. And finally, to mitigate the negative impact of nonlinearity on the estimation accuracy, pseudo-measurements are added between consecutive measurements through linear interpolation. A simple example is used to validate the proposed nonlinear indexes. The two-area four-machine system and 16-machine 68-bus system are used to evaluate the effectiveness of the proposed MSAI approach. It is shown through the Monte-Carlo method that the estimation accuracy can be improved through interpolation. In addition, a good tradeoff between estimation accuracy and computational time can be achieved effectively through the proposed MSAI approach.

In this paper, we study the adaptive fuzzy formation control of multiple autonomous agents with prioritized missions. For a platoon of autonomous agents in an unknown environment containing multiple obstacles, formation control is investigated, where each agent is modeled by a second-order nonlinear system under unknown external disturbance in the Brunovsky form. We introduce the systematic procedure of null-space-based projection to convert the prioritized multimission control problem into a behavioral control problem. Then, we further develop a class of nonlinear-fast-terminal-sliding-mode-based adaptive control strategies that combine the fuzzy logic systems by jointly considering both kinematic and dynamic levels of the agents. The proposed controllers can guarantee each individual agent to achieve the predesigned desired pattern and drive the entire systems to achieve the prescribed missions. A simulation example with five agents demonstrates the effectiveness of the algorithm. Finally, the strategies are experimentally validated using a platoon of Pioneer 3AT and 3DX mobile robots.

We report a search for new physics signals using the low energy electron recoil events in the complete data set from PandaX-II, in light of the recent event excess reported by XENON1T. The data correspond to a total exposure of 100.7 ton⋅day with liquid xenon. With robust estimates of the dominant background spectra, we perform sensitive searches on solar axions and neutrinos with enhanced magnetic moment. It is found that the axion-electron coupling g Ae &lt; 4.6 × 10 –12 for an axion mass less than 0.1 keV/ c 2 and the neutrino magnetic moment μ ν &lt; 4.9 × 10 –11 μ B at 90 % confidence level. The observed excess from XENON1T is within our experimental constraints.

In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications.

With the continuous development of the world’s aerospace industry, countries have put forward higher requirements for thermal protection materials for aerospace vehicles. As a nano porous material with ultra-low thermal conductivity, aerogel has attracted more and more attention in the thermal insulation application of aerospace vehicles. At present, the summary of aerogel used in aerospace thermal protection applications is not comprehensive. Therefore, this paper summarizes the research status of various types of aerogels for thermal protection (oxide aerogels, organic aerogels, etc.), summarizes the hot issues in the current research of various types of aerogels for thermal protection, and puts forward suggestions for the future development of various aerogels. For oxide aerogels, it is necessary to further increase their use temperature and inhibit the sintering of high-temperature resistant components. For organic aerogels, it is necessary to focus on improving the anti-ablation, thermal insulation, and mechanical properties in long-term aerobic high-temperature environments, and on this basis, find cheap raw materials to reduce costs. For carbon aerogels, it is necessary to further explore the balanced relationship between oxidation resistance, mechanics, and thermal insulation properties of materials. The purpose of this paper is to provide a reference for the further development of more efficient and reliable aerogel materials for aerospace applications in the future.

We report the search results of light dark matter through its interactions with shell electrons and nuclei, using the commissioning data from the PandaX-4T liquid xenon detector. Low energy events are selected to have an ionization-only signal between 60 to 200 photoelectrons, corresponding to a mean nuclear recoil energy from 0.77 to 2.54 keV and electronic recoil energy from 0.07 to 0.23 keV. With an effective exposure of 0.55 tonne$\cdot$year, we set the most stringent limits within a mass range from 40 $\rm{MeV/c^2}$ to 10 $\rm{GeV/c^2}$ for point-like dark matter-electron interaction, 100 MeV/c$^2$ to 10 GeV/c$^2$ for dark matter-electron interaction via a light mediator, and 3.2 to 4 $\rm{GeV/c^2}$ for dark matter-nucleon spin-independent interaction. For DM interaction with electrons, our limits are closing in on the parameter space predicted by the freeze-in and freeze-out mechanisms in the early Universe.

Previously, variations of the Yule-Walker techniques have been applied successfully to give point estimates of electromechanical modes of a power system based on measured ambient data. This paper introduces a bootstrap method to give confidence interval estimates for the electromechanical modes. Simulation results from a 19-machine model show the validation of the bootstrap method and its consistence to Monte Carlo methods. Actual measurement data taken from western North American Power Grid in 2000 are processed using the bootstrap method to give confidence interval estimates for interarea mode damping ratios. The use of multiple outputs is shown to improve the performance and tighten the confidence intervals.

This paper investigates the design of effective input signals for low-level probing of power systems. In 2005, 2006, and 2008 the Western Electricity Coordinating Council (WECC) conducted four large-scale system-wide tests of the western interconnected power system where probing signals were injected by modulating the control signal at the Celilo end of the Pacific DC intertie. A major objective of these tests is the accurate estimation of the inter-area electromechanical modes. A key aspect of any such test is the design of an effective probing signal that leads to measured outputs rich in information about the modes. This paper specifically studies low-level probing signal design for power-system identification. The paper describes the design methodology and the advantages of this new probing signal which was successfully applied during these tests. This probing input is a multi-sine signal with its frequency content focused in the range of the inter-area modes. The period of the signal is over 2 min providing high-frequency resolution. Up to 15 cycles of the signal are injected resulting in a processing gain of 15. The resulting system response is studied in the time and frequency domains. Because of the new probing signal characteristics, these results show significant improvement in the output SNR compared to previous tests.

Summary This paper develops two distributed finite‐time fault‐tolerant control algorithms for attitude synchronization of multiple spacecraft with a dynamic virtual leader in the presence of modeling uncertainties, external disturbances, and actuator faults. The leader gives commands only to a subset of the followers, and the communication flow between followers is directed. By employing a novel distributed nonsingular fast terminal sliding mode and adaptive mechanism, a distributed finite‐time fault‐tolerant control law is proposed to guarantee all the follower spacecraft that finite‐time track a dynamic virtual leader. Then utilizing three distributed finite‐time sliding mode estimators, an estimator‐based distributed finite‐time fault‐tolerant control law is proposed using only the followers' estimates of the virtual leader. Both of them do not require online identification of the actuator faults and provide robustness, finite‐time convergence, fault‐tolerant, disturbance rejection, and high control precision. Finally, numerical simulations are presented to evaluate the theoretical results. Copyright © 2015 John Wiley &amp; Sons, Ltd.

IET Control Theory & ApplicationsVolume 9, Issue 15 p. 2222-2231 Research ArticlesFree Access Coordination control design for formation reconfiguration of multiple spacecraft Ning Zhou, Ning Zhou School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081 People's Republic of ChinaSearch for more papers by this authorYuanqing Xia, Corresponding Author Yuanqing Xia xia_yuanqing@bit.edu.cn School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081 People's Republic of ChinaSearch for more papers by this author Ning Zhou, Ning Zhou School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081 People's Republic of ChinaSearch for more papers by this authorYuanqing Xia, Corresponding Author Yuanqing Xia xia_yuanqing@bit.edu.cn School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081 People's Republic of ChinaSearch for more papers by this author First published: 01 October 2015 https://doi.org/10.1049/iet-cta.2015.0144Citations: 23AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This study investigates the high precision control design of formation reconfiguration for a group of spacecraft with obstacle/collision avoidance and unknown disturbances. First, by utilising the null-space-based method, a set of pre-designed velocities are calculated for each spacecraft to perform the tasks with the obstacle/collision avoidance task has a higher priority. Second, a task-based adaptive non-singular fast terminal sliding mode coordination control algorithm (TANCCA) is proposed, which can guarantee all the spacecraft to implement the formation reconfiguration while avoiding obstacles/collisions under an unknown disturbed environment. To solve the chattering problem caused by the discontinuity of TANCCA, a modified TANCCA (MTANCCA) is then developed, which is continuous and chattering-free. Finally, the authors use the solution to the formation reconfiguration control problem for six spacecrafts in a circular Low Earth Orbit at 600 km altitude. The results of the simulation show that the MTANCCA is successful in achieving the obstacle/collision avoidance, disturbance rejection, fast convergence, and high control precision without any collisions or rules broken. 1 Introduction Spacecraft formation flying (SFF) means a group of spacecraft flying together with coupled dynamic states, and research in this area has become an increasingly popular subject in recent years. Formation with a definite configuration could deem to work as a virtual monolithic spacecraft, it will greatly enhance system performance by distributing the task from a monolithic spacecraft to several small spacecraft, especially for some capabilities which are unachievable for a single monolithic spacecraft [1]. Replacing large and complex spacecraft with an array of simpler micro-satellites can bring out new possibilities and opportunities of cost reduction, redundancy, and improve resolution aspects of onboard payload, thus SFF is a new method of performing space operations. In spaceflight, obstacle/collision avoidance is the process of preventing a spacecraft from colliding with any other spacecraft, object or space junk. In a tightly flying cluster of spacecraft, obstacle/collision avoidance is an essential concern while fulfilling specific and varied missions. Furthermore, the disturbance forces working on a spacecraft in orbit are mainly caused by gravitational perturbations, atmospheric drag, solar radiation, solar wind, and third-body perturbing forces [2]. Although the disturbances are small, they should not be ignored because over time large deviations will occur without any treatment. From the aforementioned, research on the problem of SFF with obstacle/collision avoidance and disturbances has important practical significance and application potential. During the past decade, many studies have been published on spacecraft obstacle or collision avoidance. Using the null-space-based (NSB) concept, Schlanbusch et al. [3] investigated the spacecraft formation and collision avoidance problem, and a cooperative control method was developed. Wang and Schaub [4] considered a two-spacecraft collision avoidance problem and proposed a control algorithm where the cluster internal coulomb forces is employed. Slater et al. [5] discussed the collision probability in a satellites formation under the influence of orbital disturbances, and presented requirements for velocity corrections to avoid collision. Using the time-series analysis method, Qi and Jia [6] developed a new switching control scheme for spacecraft collision avoidance. Under the chaser's thruster failure in radial direction, Qi and Jia [7] considered the collision avoidance maneuver problem, and proposed a switching control algorithm under constant thrust. A satellites formation problem is discussed in [8], and a sequential optimisation algorithm to collision avoidance is surveyed using a semi-analytic approach. Comparison with the above results, this study considers the problem of finite-time convergence which is especially useful for high accuracy SFF control. Sliding mode control (SMC) is one of the most effective robust control method to achieve the robustness and invariant property to uncertainties, system parameter perturbations, and bounded external disturbances [9]. Comparing with the traditional linear hyperplane-based SMC, the terminal sliding mode (TSM) control provides superior properties, i.e. fast and finite-time convergence, and higher control precision. However, the initial TSM control exists two disadvantages. The first is the singular problem, the second is that the equilibrium points cannot be converged quickly when the initial states of systems are far away from them. Therefore, a non-singular TSM (NTSM) control was proposed in [10] to eliminate the singularity problem, and a so-called FTSM was proposed in [11] which shows faster convergence even if the initial states are far away from their equilibrium points, but it brings a singular problem. In [12], a modified TSM is proposed to overcome the singularity problem which can also mitigate some drawbacks in the linear SMC. Zou et al. [13] extended it to a modified FTSM, and a finite-time attitude tracking control scheme was proposed based on FTSM control and Chebyshev neural network for a single spacecraft. Using this modified FTSM, a distributed attitude coordination control scheme was proposed in [14] for a group of spacecraft under the undirected communication topology. Lu and Xia [15] investigated the finite-time attitude tracking problem for a spacecraft with external disturbances and inertia uncertainties, and several chattering-free control schemes are proposed based on the fast non-singular TSM surface and adaptive control mechanism. In [16, 17], the problem of finite-time attitude synchronisation and tracking control for multiple spacecraft was investigated by virtue of the FTSM, network graph theory and adaptive control, and several decentralised finite-time control schemes were presented to deal with the problem of fast, finite-time convergence and disturbance rejection. However, up to now, none of the existing results focused on the problem of high precision SFF with obstacle/collision avoidance. In this paper, a new task-based non-singular fast TSM (TNFTSM) is designed based on a pre-designed velocity. This is different to the desired trajectory which is 'given' directly in the previous works. Moreover this kind of design is more simpler and easier to perform the tasks. This study aims to develop a coordination control algorithm for multiple spacecraft to perform the high precision formation reconfiguration mission while avoiding obstacles/collisions in the presence of disturbances. The concept of compound control is used in the design of the control methodology. By employing this concept, many excellent results have already been published in authoritative journals and press [15, 18–22]. It is worth noting that though some individual control techniques have been well known (i.e. adaptive control, SMC etc.), research on compound control still has caught much attention of researchers because of its merit of superior control performance (i.e. strong robustness, fast convergence, high control precision etc.). The main contributions of this paper are stated as follows: Utilise the pre-designed velocity, a new TNFTSM is designed based on the NSB concept, then a novel MTANCCA is constructed by virtue of the adaptive control and TNFTSM to solve the formation reconfiguration of multiple spacecraft in the obstacle environment. Under the disturbed environment, the designed control strategy can guarantee a group of spacecraft to reconfigure a formation successfully while avoiding obstacles/collisions without using any prior information (i.e. the upper bounds of the unknown disturbances). Compared with the previous work [3], the developed methodology can provide higher control precision, faster convergence, better adaptability and stronger robustness, and the validity has been confirmed by theoretical proof and numerical simulation. The rest of this paper is organised as follows. The spacecraft formation dynamics, assumption, and relative lemmas are given in the following section. In Section 3, the pre-designed velocity is calculated using the NSB method. The TNFTSM is designed and two coordination control algorithms are developed in Section 4. Two simulation examples are given in Section 5 to demonstrate the effectiveness of the proposed algorithm. Finally, the conclusions are given in Section 6. Notation : Throughout this paper, an abbreviation 'TANCCA' is used to be short for 'task-based adaptive non-singular fast TSM coordination control algorithm', in which 'N' is short for 'non-singular fast TSM'. denotes the time derivative of a vector x, i.e. , and . ∥​· ​ ∥ denotes the Euclidian norm of a vector and the induced ℒ2 norm of a matrix. The cross-product operator is denoted S (·), such that S (x) y = x × y. ℱ(·) denotes the reference frame, and in particular, the standard Earth-Centered Inertial (ECI) frame is denoted ℱi. We denote be the angular velocity of ℱa relative to ℱb, referenced in ℱc. Matrices representing rotation or coordinate transformation from ℱa to ℱb are denoted as . fulfilling which is the special orthogonal group of order three, and its time derivative can be written as . Denote sigα(x) = |x |α sign(x), where α > 0. If 0 < alpha < 1, then function sigα(x) is a continuous non-smooth function. If , then denote |x | = [|x1 |, …, |xn |]T, , and sigα(x) = [sigα(x1), …, sigα(xn)]T. Define the saturation function as follows: where φk > 0 is the boundary-layer thickness, for k = 1, …, n. In addition, denote sat(x) = [sat(x1), …, sat(xn)]T, if x = [x1, …, xn]T. We will omit to write arguments of a vector or matrix if it is sufficiently explicit in the context. 2 Preliminaries In this paper, we consider a group of spacecraft with n followers and one leader. Three reference frames are need for the further analysis, which are given as follows: (i) Inertial reference frame ℱk; (ii) leader orbit reference frame ℱl; and (iii) follower orbit reference frame ℱf, k, for k = 1, …, n. The k th follower spacecraft's formation dynamics is modelled as [23] (1)where pk = [pk, 1, pk, 2, pk, 3]T is the relative orbit position vector expressed in ℱl, mf,k is the mass of the k th follower, is a skew-symmetric matrix, , denotes the identity matrix, is the vector pointing from the centre of Earth to the leader spacecraft in ℱk, rl is the distance from the centre of Earth to the leader spacecraft, rf,k denotes the vector pointing from the centre of the Earth to the frame origin, rf,k denotes distance from the centre of the Earth to the frame origin, and are the orbital angular velocity and angular acceleration, respectively, and , , vl and al are the linear velocity and acceleration of the leader spacecraft in ℱk, respectively. is a non-linear term, and μ ≃ GMe, G is the gravitational constant, Me is the mass of the Earth. is the composite relative control force, where and are the actuator forces of the k th follower spacecraft and the leader spacecraft, respectively. is the composite perturbation force, where and are the perturbation terms of the k th follower spacecraft and the leader spacecraft due to external effects, respectively. It is assumed that the leader spacecraft is perfectly controlled in its orbit, so that fal = − fdl, such that Fa, k = faf, k and Fd, k = fdf, k. Noting that if , the follower is located at the centre of the orbit, thus there is a singularity in (1) and according to [24] the conclusion pertains to the case and only to the case when the state space is . Assumption 1.The composite perturbation force Fd, k is assumed to be bounded as ∥Fd, k ∥ ≤ θk with θk > 0. Denote be the estimate of θk, which will be used to reject the disturbances and will be adjusted by adaptive mechanism in the following design. is the estimation error, which is defined as . The control objective of this paper is to design a set of task-based coordination control laws for a group spacecraft to reconfigure and maintain a rigid formation in the present of unknown disturbance with high control precision, fast convergence, good adaptability, and robustness while avoiding obstacles/collisions. 3 Desired velocity design for formation and obstacle/collision avoidance The NSB method is a task-priority kinematic acting on the dynamics through the desired velocity [3, 25]. In this section, by employing the NSB approach, the pre-designed velocity for each spacecraft can be calculated by virtue of two task functions, i.e. the formation σf and obstacle/collision avoidance tasks σk, o for k = 1, …, n, which will be treated as the desired velocity in the following design. The obstacle/collision avoidance task will be given a higher priority. Considering a system of n follower spacecraft, the merged desired velocity vector from the two presented tasks is given by (2)where , denotes the desired velocity for the obstacle/collision avoidance task, and each component vector is given by , pk, o denotes the position of the nearest obstacle for the k th spacecraft, is the relative velocity for the obstacle and the k th spacecraft, denotes the identity matrix. marks a virtual sphere σk, od = dk, where dk is the minimum allowed distance between the k th spacecraft and an obstacle. is the task function for obstacle/collision avoidance. Define , if the obstacle/collision avoidance task is active, then , otherwise, . is the Jacobian matrix, is a unity vector pointing at the nearest obstacle, and we denote . λk, o > 0 is a state-dependent gain to be defined later. is the desired velocity for formation task, where Λf is a positive definite matrix of gains. is the Jacobian matrix, and (3)with x = 1 − (1/n), y = − (1/n), which has one zero eigenvalue and (n − 1) eigenvalues equal to one, thus rank(Jf) = 2n. The pseudo-inverse of Jf is . , σfd denotes the coordinates of all spacecraft in the desired configuration, σf = [(p1 − pb)T, …, (pn − pb)T]T is the formation task function, where is the coordinate of the barycentre. If the desired formation is fixed, then will hold. 4 Controller design for formation reconfiguration Define the desired trajectory as , the corresponding desired velocity and acceleration defined likewise, which are all bounded functions and can be calculated using (2). We aim to design a coordination control law for each spacecraft to perform different tasks. Before moving on, some results are required for the following analysis. Lemma 1 [26].Suppose n and m are two positive real numbers, and a ≥ 0, b ≥ 0. Then, for any constant Lemma 2 [27].For , i = 1, …, n, a ∈ (0, 1), . Lemma 3 [28].An extended Lyapunov description of finite-time stability can be given with the form of FTSM as and the settling time can be given by where μ1 > 0, μ2 > 0, 0 < ν < 1, and V (x0) is the initial value of V (x). 4.1 TNFTSM design Define the task-based reference trajectories for each follower as and , where , , c1 and c2 are two positive constants, , for k = 1, …, n, and for j = 1, 2, 3, r = (r1 /r2), where r1 and r2 are positive odd integers, 1/2 < r < 1, ϕ denotes a small positive constant, l1 = (2 − r) ϕr −1, l2 = (r − 1)ϕr −2. , , and Design the TNFTSM as (4)where , sk = [sk, 1, sk, 2, sk, 3]T, and . Remark 1.Following [12], the choice of l1 and l2 can make sure that the function and its time derivative are continuous. In the case that , the TNFTSM is switched into the general sliding manifold when enters the region . Furthermore, the singularity problem in the case that and ϕ = 0 can also be overcome. Thus the TNFTSM has the advantages of the conventional sliding mode and the FTSM together. Furthermore, we restrict 1/2 < r < 1 for the purpose of avoiding a negative fractional powers in which will be used in the controller design. 4.2 Control law design Theorem 1.Consider the k th follower spacecraft's formation dynamics (1) with the TANCCA in the following equations (5) (6)where ν = (ν1 /ν2), ν1 and ν2 are positive odd integers, 0 < ν < 1, sign(·) denotes the sign function, Ξs, k = diag{ξs, k, ξs, k, ξs, k}, Ξν, k = diag{ξν, k, ξν, k, ξν, k}, and Ξd, k = diag{ξd, k, ξd, k, ξd, k}, ξs, k, ξν, k, ξd, k, γk, and χk are positive constants. Suppose that Assumption 1 satisfied, we conclude that If there is no conflict between the two tasks, then they will be fulfilled simultaneously. Meanwhile, the position error and velocity error for k = 1, …, n, j = 1, 2, 3 will converge to the regions and in finite time, respectively. If the two tasks are conflicting, λk, o is chosen as with ϵk > 0, then the formation task can be fulfilled after avoiding obstacles/collisions. Furthermore, the position error and velocity error for k = 1, …, n, j = 1, 2, 3 can converge to the regions and in finite time, respectively. Proof.Construct the suitable Lyapunov candidate function as (7)with , , where γo, γf are design parameters, , mf = mf,k, , and Γ = diag{γ1, …, γn} is a symmetric positive definite constant matrix.There are three main steps in the following proof.Step 1: Prove the boundedness of sk and .Using (4)–(6), it obtains the derivative of V1 as Considering Assumption 1 and using the fact that is a skew-symmetric matrix, we further obtain (8)Using Lemma 1, it is easy to obtain the following inequality Then (8) can be rewritten as (9)Let and , where . Then, (9) becomes (10)Multiplying both sides by eδ1 t, (10) can be expressed as (11)Integrating (11) over [0, t], it follows that (12)Noting that 0 < eδ1 t < 1 and (δ2 /δ1)eδ1 t > 0, we have [V1 (0) − (δ2 /δ1)]eδ1 t ≤ V1 (0). Then the above equation becomes (13)Then, it can be known that sk and are bounded. Therefore, there exists a positive constant ξ0 such that ξ0 ≥ ɛ, where ɛ = max {ɛ1, …, ɛn}, for k = 1, …, n. Choose ξd, k such that ξd, k > ξ0.Step 2: Prove the finite-time convergence of s, , and .Define . Invoking Lemma 3, it follows that (14)where η1 = 2(ξs, m /mf), , ς = (ν + 1/2), ξs, m = min {ξs, 1, …, ξs, n}, and ξν, m = min {ξν, 1, …, ξν, n}, thus it concludes that the sliding manifold s converges to 0 in finite time.From this point, three different cases should be discussed:Case 1: If (k = 1, …, n, j = 1, 2, 3) is achieved, we can obtain . Then and converge to 0 in finite time.Case 2: If and (k = 1, …, n, j = 1, 2, 3), has converged to the region in finite time. Using (4), it yields . Therefore, will converge to the region in finite time.Case 3: If and (k = 1, …, n, j = 1, 2, 3), it will lead to sk, j ≠ 0, this case will not occur.From the above discussion, we conclude that and will converge to the regions , in finite time, respectively.Step 3: Prove the stability of the tasks.Considering the following two different cases [23, 29]: If there is no conflict between the two tasks, then [30]. Using (2), it obtains the derivative of V2 as where , . Thus the tasks are stable. If the tasks are conflicting, i.e. the obstacle/collision avoidance task is active. can be written as where , and ∥Jo ∥ = ∥ Jf ∥ =1 is used. Thus is out of control. In order to guarantee the stability of the tasks, we have to remove the counteracting components when the tasks are conflicting, i.e. remove the contribution from . To do this, V2 should be redefined as , then . Thus the conclusion is also true in this situation. Furthermore, the velocity error in the control law should dominate the position error, because the behavioural control method is a kinematic working on the dynamics through the desired velocity. By inserting (2) into (4), and using the fact that the contribution from has been removed, the following condition should be satisfied (15)From a conservative point of view, take the norm on both sides of (15) and manipulate as an equality, then the minimum positive value of λk, o can be calculated as (16)where , , and + for k = 1, …, n. Using , then similar result as (16) can be obtained to make sure that the velocity error is dominating the position error in the term of the control law. Choose , where ϵk > 0 is a robust term to reject, e.g. measurement noise. Moreover, the desired relative distance between spacecraft should be chosen larger than dk to keep each spacecraft outside of the collision sphere for all time. If all the above limitations are satisfied, then the formation task can be fulfilled after avoiding obstacles/collisions. This completes the proof. □ Remark 2.When we integrate the NSB method with TNFTSM, an additional problem should be considered, i.e. a constraint about the design parameter λk, o should be designed and satisfied to ensure that the velocity error dominates the position error in the control law, because the NSB method is a task-priority kinematic acting on the dynamics through the desired velocity. Remark 3.Adaptive mechanism is applied to reject the influence of disturbances without using any prior information (i.e. the upper bounds information of the disturbances). According to the merit of the adaptive control, the design parameter can provide better adaptability and stronger robustness of the system compared with the existing results [3]. 4.3 Modified control law design The existence of the sign function in the control law (5) makes it discontinuous across the surface s, thus leading to control chattering. We remedy this situation by smoothing out the control discontinuity in a thin boundary layer neighboring the switching surface [31]. To do this, the sign function in the control law (5) can be replaced by a saturation function, thus the MTANCCA is given by (17) (18)where s ′k = [s ′k, 1, s ′k, 2, s ′k, 3]T, s ′k, j = sk, j − φk, j sat(sk, j), φk, j is the boundary-layer thickness, and s ′ = [s ′1, …, s ′n]T. Theorem 2.Consider the k th follower spacecraft's formation dynamics (1) with the MTANCCA (17) and (18). Suppose that Assumption 1 satisfied, we conclude that If there is no conflict between the two tasks, then the position error and velocity error for k = 1, …, n, j = 1, 2, 3 will converge to the regions and in finite time, respectively. If the tasks are conflicting, the design parameter is chosen as , , , then the formation task can be fulfilled after the obstacle/collision avoidance task. Furthermore, the position error and velocity error for k = 1, …, n, j = 1, 2, 3 can converge to the regions and in finite time, respectively. Proof.Redefine the candidate Lyapunov function as (19)with , . Three aspects are considered in the following proof.Step 1: Prove the boundedness of s ′k and . Using the fact that |s ′k, j | = 0 for |sk, j | < φk, j and |s ′k, j | = s ′k, j sat (sk, j) for |sk, j | ≥ φk, j, and , it obtains the derivative of V3 as (20)Following Theorem 1, it yields s ′k and are bounded.Step 2: Prove the finite-time convergence of s ′, , and . Choose ξd, k > ξ0, and use Lemma 3, it follows that (21)where , thus it concludes that s ′ converges to 0 in finite time, i.e. s converges to in finite time, where , φ = [φ1, …, φn]T, φk = [φk, 1, φk, 2, φk, 3]T, φk, j > 0 is the boundary-layer thickness for k = 1, …, n, j = 1, 2, 3.Then three different cases will be discussed:Case 1: If (k = 1, …, n, j = 1, 2, 3) is achieved, we can obtain . Then and converge to 0 in finite time.Case 2: If and (k = 1, …, n, j = 1, 2, 3), has converged to the region in finite time. Using (4), one has , where . Therefore, we conclude that will converge to the region in finite time.Case 3: If and (k = 1, …, n, j = 1, 2, 3), then we have (22)where . The preceding equation can be rewritten in the following two forms (23) (24)From (23), when , (23) is still in the form of the FTSM, will converge to the region in finite time.From (24), when , (24) is still in the form of the FTSM, will converge to the region in finite time.Therefore, will converge to the region in finite time, where Furthermore, from (22), will converge to the region in finite time.From the above discussion, and noting that , we conclude that and will converge to the regions , in finite time, respectively.Step 3: Prove the stability of the tasks.The derivative of V4 is the same as V2, and similar analysis and result can be obtained as Theorem 1.This completes the proof. □ Remark 4.Slotine and Coetsee [32] states a particular situation that if there is a frontal collision, the projection along the tangential direction will be null, thus leading to a local minimum. This situation can be avoided by the presence of measurement noise. Remark 5.The singular, discontinuity, and chattering problems are the common issues in the design of the FTSM control. In this paper, these problems are solved successfully in the modified TNFTSM (MTNFTSM) s ′ and the control law (17). First, a smoother switch is produced by switching from terminal to linear sliding manifold when , thus the TNFTSM designed in this paper is non-singular. Second, the discontinuity of the sliding mode will cause the discontinuity of the control law, and may further affect the control performance. The MTNFTSM designed in this paper is continuous by employing a smoother switch and a saturation function. Finally, the chattering problem is widely existed in sliding mode control design due to the discontinuous design of the robust control law, it is obvious that the proposed method in Theorem 2 is continuous and chattering-free. 5 Illustrative examples To evaluate the control performance of the proposed controller, numerical simulations are carried out using (1) in conjunction with the MTANCCA (17) and (18) for the formation reconfiguration control problem. Six spacecraft are considered to relocate and shape a new formation while avoiding collisions [3], where a leader is included therein. To increase the authenticity and reliability of the simulation results, the effect of model uncertainties on the performance of the proposed controller is investigated. The mass of each spacecraft is considered to be m k = mk + ▵ mk, where mk = 100 kg is the nominal mass of spacecraft k, ▵mk = 1 kg is the parameter perturbation. The leader spacecraft is assumed to be perfectly controlled in a circular Low Earth Orbit of altitude 600 km, the inclination (i), the argument of perigee (ω), and the right ascension of the ascending node (Ω) all at 0∘. The measured states are and , where is a compact set, δ0 is a small constant. Assume that the maximum control force is 100 N, then we can calculate that the biggest acceleration is about 1 m/s2 from the nominal mass of the spacecraft and (1). The parameters for the simulation, such as the task parameters and controller parameters etc., are summarised in Table 1, where , k = 1, …, 5 are the initial values of . Table 1. Numerical simulation parameters Parameter name Value task parameters d = 10, Λf = 0.8I, ϵk = 1, k = 1, …, 5 controller parameters ξs, k = 30, ξr, k = 10, ξd, k = 0.1, c1 = 0.8, c2 = 0.8, r1 = ν1 = 3, r2 = ν2 = 5, ϕ = 0.001, φk, j = 0.001, k = 1, …, 5, j = 1, 2, 3 adaptation parameters γk = 0.8, χk

IET Generation, Transmission & DistributionVolume 9, Issue 15 p. 2409-2418 Research ArticleFree Access Stochastic subspace identification-based approach for tracking inter-area oscillatory modes in bulk power system utilising synchrophasor measurements Tao Jiang, Tao Jiang Department of Electrical Engineering, Northeast Dianli University, Jilin, 132012 People's Republic of ChinaSearch for more papers by this authorHaoyu Yuan, Haoyu Yuan Electrical Engineering and Computer Science Department, University of Tennessee, Knoxville, TN, 37996 USASearch for more papers by this authorHongjie Jia, Corresponding Author Hongjie Jia hjjia@tju.edu.cn Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072 People's Republic of ChinaSearch for more papers by this authorNing Zhou, Ning Zhou Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY, 13902 USASearch for more papers by this authorFangxing Li, Fangxing Li Electrical Engineering and Computer Science Department, University of Tennessee, Knoxville, TN, 37996 USASearch for more papers by this author Tao Jiang, Tao Jiang Department of Electrical Engineering, Northeast Dianli University, Jilin, 132012 People's Republic of ChinaSearch for more papers by this authorHaoyu Yuan, Haoyu Yuan Electrical Engineering and Computer Science Department, University of Tennessee, Knoxville, TN, 37996 USASearch for more papers by this authorHongjie Jia, Corresponding Author Hongjie Jia hjjia@tju.edu.cn Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072 People's Republic of ChinaSearch for more papers by this authorNing Zhou, Ning Zhou Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY, 13902 USASearch for more papers by this authorFangxing Li, Fangxing Li Electrical Engineering and Computer Science Department, University of Tennessee, Knoxville, TN, 37996 USASearch for more papers by this author First published: 01 November 2015 https://doi.org/10.1049/iet-gtd.2015.0184Citations: 23AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Stochastic subspace identification (SSI) methods have been widely employed for oscillatory mode identification on probing and ambient data and are reported to have good performances. This work proposes a novel SSI-based approach for identifying dominant oscillatory mode from measurement data and extends the application of SSI to ringdown condition. The proposed approach first constructs an initial cluster of eigenvalues from SSI with repetitive calculations and then utilises a novel hierarchical clustering method to extract the dominant modes from the initial cluster. The repetitive calculations within the SSI are performed through varying the model order over a range defined by a novel initial order determination process. By doing so the challenge of model order determination for SSI-based methods is resolved. Moreover, benefiting from the repetitive calculations and the clustering process, the proposed approach is highly immune to prevalent noises in the measurements. Finally, the proposed approach is applied and validated on the field-measurement data from the phasor measurement units of China Southern Power Grid (CSG) through comparisons with Prony, ARMAX, and Monte Carlo methods. Test results demonstrate that the proposed approach performs with high accuracy, robustness, and efficiency in CSG. 1 Introduction Electromechanical oscillation is one of the inherent characteristics in power systems [1]. An undamped oscillation, if not controlled properly, can cause wide-spread blackouts [2]. In order to prevent such blackouts, timely and accurate information of electromechanical oscillation is critical. Currently, the approaches for extracting the electromechanical oscillatory information for bulk power systems can be classified into two categories, model-based and measurement-based approaches. The model-based approaches detect the oscillatory modes through eigenvalues analysis by linearising dynamic model of a power system at a certain operating point [3]. This kind of approach is capable of calculating all the electromechanical oscillatory modes and the corresponding controllability, observability and participation factors for all generators. Despite the versatility, there are several issues regarding such approaches: (i) accurate modelling of the whole system is difficult, (ii) computational burden dramatically increases when system size scales up, (iii) models and parameters cannot be updated in real time, and (iv) the result is only effective for a bounded neighbourhood of a given operating point. Therefore, such approaches are suitable for off-line application of small-signal stability analysis (SSSA) rather than real-time deployment. On the other hand, the measurement-based approaches are alternatives to resolve these issues and complement the model-based approaches [4, 5]. Inspired by the initiative of the wide-area measurement systems (WAMSs) which consist of phasor measurement units (PMUs), the measurement-based approaches, also known as ModeMeter [5], are proposed to extract the electromechanical oscillatory modes and damping from the measurement. One appealing characteristics of such approaches is that they do not require detailed power system model nor accurate parameters. Moreover, with the real-time measurement from WAMS, the result from such approaches can represent the actual dynamic features of the system. As a result, measurement-based approaches have been studied extensively in online dynamic stability assessment [6–12]. According to the measurement data used, these approaches can be further classified into three categories: (i) probing, (ii) ringdown, and (iii) ambient [7, 8]. The probing-based approaches estimate electromechanical oscillatory modes and their damping from the probing responses triggered by intentionally injection of low-level pseudo-random noise into the system. Western Electricity Coordinating Council (WECC) has conducted a number of system tests by injecting probing signals into Pacific high voltage direct current (HVDC) Intertie to identify the inter-area electromechanical oscillatory modes and facilitate the ModeMeter development [9–11]. The ringdown-based approach identifies modes from ringdown data incurred by sudden changes, such as line tripping, generator fault, and bus fault. Several algorithms have been developed, including Teager-Kaiser operator [12], Kalman filter [13], stepwise-regression-based Prony [7], and robust recursive least square [14]. These algorithms have been demonstrated to be effective for ringdown data, but not as effective for ambient data. For ambient data, Zhou et al. [8] adopted ARMAX to identify the dominant oscillatory modes; Ni et al. [15] applied reference channels and covariance-driven stochastic subspace identification (COV-SSI) for online SSSA; Dosiek et al. [16] comparatively reviewed the existing ambient electromechanical oscillatory mode estimation approaches including transfer function, spectral, frequency domain decomposition, channel matching, and subspace methods. Among various methods, stochastic subspace identification (SSI) exhibits good performance for both probing and ambient conditions [11, 15, 17]. Imbedded in SSI approaches, the determination of model order remains a crucial factor which highly affects the performance of SSI. To deal with this issue, Ghasemi et al. [18] proposed the largest drop in singular values to determine the model order for SSI. However, it is reported this method is only suitable for simple cases. Moreover, in practice, in order to suppress signal offsets and improve identification accuracy, additional trivial modes are intentionally included in the SSI. Consequently, in the results of SSI, the trivial modes which are intentionally added are mixed with the dominant modes. This brings new challenges for accurate identification of the dominant modes from various mixed modes. With the motivation to deal with these issues, a general and reliable oscillatory mode identification approach based on SSI is proposed in this paper. Since the performances of SSI in the probing and ambient conditions have been validated, this paper focuses on detecting dominate oscillatory mode from ringdown data using SSI. The proposed approach employs the data-driven SSI (DATA-SSI) to identify electromechanical oscillatory modes from the ringdown data. In order to address the problem of model order determination, a novel strategy is proposed. First, the initial model order is determined according to cumulative contribution rate of singular values (CCRSV). Second, SSI with repetitive calculations is conducted for model orders from the initial order to the predefined maximum order. A novel hierarchical clustering (HC) method is then used to extract the dominant oscillatory modes from the repetitive results. A refining process is further applied to ensure higher accuracy. With the above strategy, a range of model orders are considered in SSI to compress signal offsets and HC as well as the refining process leads to a final result with high accuracy. This paper is organised as follows: In Section 2, the modal identification using DATA-SSI is reviewed. In Section 3, the proposed approach including the model order determination and HC is developed. In Section 4, the developed approach is validated on the wide-area field-measurement data acquired from PMUs of China Southern Power Grid (CSG). Moreover, it is compared with Prony and ARMAX methods under different SNRs and with Monte Carlo methods to demonstrate its efficiency. In Section 5, conclusions are drawn. 2 Oscillatory mode identification using SSI 2.1 Wide-area measurement-based approach Early model-based approaches are based on linearised continuous time models and time domain simulation of power systems. Due to the advent of PMUs, measurement-based approaches are proposed to estimate modes using discrete-time measurements [19]. With the measurements from synchrophasors, a power system can be represented by an n-order discrete-time state-space model [11, 19] (1)where x ∈ Rn, y ∈ Rl, and u ∈ Rm are state, output, and input vectors, respectively; A ∈ Rn×n, B ∈ Rn×m, C ∈ Rl×n, and D ∈ Rl×m are state, input, output, and feed forward matrices, respectively; w ∈ Rn and v ∈ Rl are the random white sequences of process disturbance and measurement noises, respectively; T is the sampling interval; and k is the sampling index. Calculate the eigenvalues of A and transform them from discrete time domain to continuous time domain. The corresponding eigenvalues in continuous time domain can be expressed as follows [11]: (2)where βi are the eigenvalues of A in discrete time domain and αi are the corresponding eigenvalues in continuous time domain. In (2), αi may be a real or complex eigenvalue. A real αi corresponds to a non-oscillatory mode; while a complex αi occurs in conjugate pair and corresponds to an oscillatory mode. The frequency of the oscillation fi and its damping ratio ζi are given by (3)Therefore, a typical measurement-based mode identification procedure in power system can be described as determine the state matrix A via the field-measurement data, and then identify the electromechanical oscillatory mode of power system by (2) and (3) [11]. Several techniques have been developed to estimate A. In this paper, SSI is used. 2.2 SSI based on Cholesky decomposition SSI is a powerful system identification technique. Differentiated by the projection matrix, it can be divided into DATA-SSI and COV-SSI. Hence the result of COV-SSI is highly affected by the data errors in the calculation process, DATA-SSI is used here to estimate A. In order to enhance the computational efficiency of DATA-SSI, Cholesky decomposition is adopted to calculate projection matrix. The process can be briefly represented as follows [20]: i. Assemble the wide-area measurement data as the following equation: (4)where M is the length of the wide-area measurement data. ii. From Hankel matrices H which consists of Yp and Yf [6, 8] (5)where a is equal to 2n/l with l being the number of measurement channels, b is the length of the wide-area measurement data and b ≤ M. iii. Apply LQ decomposition to Hankel matrices H to obtain the matrices L0 and Q0: (6) iv. Calculate projection weighting matrix. With L0, L1 = L0 (w/2 + 1: w, :), L′ = L1(:, 1:w), and L″ = L1(L1)T, where w is the number of rows of L0. Implement Cholesky decomposition for L″ to achieve weighting matrix L, and then the projection weighting matrix W can be calculated according to the following equation: (7) v. Apply singular value decomposition (SVD) to W. (8)where S1 ∈ Rr is a diagonal matrix composing of the non-zero singular-values, U, V, U1, V1, U2, and V2 are unitary matrices obtained from the SVD process, respectively. vi. Calculate observable subspaces O ∈ R2k×r using L, U1, and S1 (9)where Oi−1 ∈ Rk×r and Oi ∈ Rk×r are the sub-matrices of O, respectively. vii. Determine the state matrix A (10)where superscript ‘+’ denotes pseudo-inverse. viii. Calculate the eigenvalues of A and convert them into continuous time domain via (2). 3 Proposed approach As discussed in Section 1, determining the model order and discriminating dominant modes from various mixed modes are two challenges for SSI. In order to deal with these two issues and enhance the accuracy of the identification, [21] proposed mode matching pursuit method to identify dominant modes in CSG. The mode matching pursuit method is essentially an ‘offline calculation and online matching’ approach in which the model order and accuracy of identification depend largely on the offline result. However, without considering real-time measurement, the system parameters and operating point of the offline study may differ from the actual situation which may lead to failure to capture some of the dominant modes. To improve the method in [21], this section proposes a general and effective approach to determine the model order and discriminate dominant modes using measurement data. 3.1 Initial model order determination In Section 2, DATA-SSI was introduced to determine A where the determination of the order of S1 in (8) plays a key role. Ideally, the model order can be determined by examining S matrix in (8) obtained by SVD. However, it is usually not feasible for field-measurement from practical system for that S is most likely a full rank matrix, shown in the following equation: (11)where the diagonal elements of S2 are close, but not equal to 0, which leaves it difficult in determining S1 and the model order. Moreover, in practice, in order not to miss any dominant electromechanical oscillatory modes, the initial model order is usually overestimated. However, the overestimation may bring in unnecessary trivial modes. To resolve this issue, a general and effective strategy of order selection is developed by referring to determining the principal components in multivariate statistical analysis. This is based on CCRSV. Let S = diag[λ1, λ2, …, λj] in (11). CCRSV is defined as follows: (12)where ηk is the CCRSV of the first k singular values. Moreover, the model order can be selected through (13) with ηk, (13)where η0 is a setting value. Note that a reasonable value of η0 is very important. Based on the definition of ηk in (12), the value of η0 should be between 0 (η1) and 1 (ηj). As will be mentioned next, the proposed approach takes repetitive calculations for model orders from r0 to rmax, and the value of η0 determines the value of r0. According to (13), choice of η0 is essentially a trade-off between the computation time and the estimation accuracy. In the case study in Section 4, η0 in (13) is set to 0.5 as referred to [22]. In addition, the model order should be even because the oscillatory modes occur in conjugate pairs. This is guaranteed by (13). 3.2 SSI with repetitive calculation Using the determined model order, the state matrix A can be obtained through (8)–(10) and the eigenvalues of A are then calculated. However, unlike model-based approach that determines the electromechanical modes using participation factors and relevance ratios, SSI is unable to directly identify the dominant oscillatory modes. To distinguish the dominant oscillatory modes from the trivial modes, this paper proposes SSI with repetitive calculation of Steps vi–viii in Section 2 for a range of model orders from r0 to rmax, the predefined maximum order. On a set of field-measurement data, the first five steps of SSI shown in Section 2 are implemented. For the sixth step, observable subspaces are repetitively calculated for model orders from r0 to rmax according to (9). The following two steps, i.e. determination of state matrix and calculation of eigenvalues are also repetitively calculated for different model orders. All the calculated eigenvalues are then grouped for further analysis. It is noted that although a range of model orders are considered repetitively, the first five steps are implemented only once while only the last three steps are repetitively implemented. Therefore the computational burden is largely reduced compared with approaches that re-run the whole SSI for different model orders. There are two benefits from the repetitive calculations: (i) different model orders are included which minimises the possibility of missing any dominant oscillatory mode; and (ii) through the repetitive calculations, the dominant oscillatory modes will be detected more frequently than the trivial mode and thus can be distinguished from the trivial ones through proper techniques. 3.3 Preprocessing the initial results In order to filter out some trivial modes, a preprocessing is applied to the results obtained in Section 3.2. The detailed of the preprocessing is represented as follows: Filter out the modes with a positive real eigenvalue in discrete time domain which correspond to non-oscillatory modes in the continuous time domain. Filter out the modes that do not satisfy . These modes are outside the range of the frequency band of the power system low-frequency oscillation, 0.1–2 Hz. As complex eigenvalues occur in conjugate pairs, the preprocessing only retains the eigenvalues with positive imagery parts in order to lighten the computational burden for clustering. 3.4 Hierarchical clustering Although the modes obtained by the above preprocessing will locate only in the range of 0.1–2 Hz, it is still not enough to distinguish the dominant modes since those trivial modes also exist in this range. In order to discriminate the dominant electromechanical oscillatory modes, a novel HC algorithm is further developed. The HC algorithm divides the preprocessed results into several clusters through the stepwise classification, refines the remaining clusters of the last clustering step, and extracts the dominant mode from the refined clusters. The detailed procedure of HC is described as follows: (a) Get initial cluster. For a given time window of field-measurement data, the initial cluster is formed through repetitive calculations within the SSI described in Section 3.2 and preprocessed by the methods described in Section 3.3. (b) Stepwise classification. Calculate vi, the standard deviation (Std.), and Ni, the number of components, for each cluster i. If Ni < N0, delete this cluster; if Ni ≥ N0 and vi>v0, divide this cluster into two new clusters; if Ni ≥ N0 and vi ≤ v0, retain this cluster. Repeat step (b) until all the remaining clusters satisfy that Ni ≥ N0 and vi ≤ v0. In this step, the choices of N0 and v0 are critical for Stepwise classification and identifying the dominant mode clusters. Theoretically, the dominant modes can be calculated from estimated A for each model order, and therefore the number of components in the dominant mode clusters should be equal to p, the total number of repetitive calculations within the SSI. However, considering noise and computational error, it is possible that for some of the repetitive calculations, the dominant mode is not detected. Thus the choice of N0 should be less than p, but not too small in which case the trivial modes may meet this criterion. In our case study, N0 is set to be p/2 which indicates that the dominant mode must be detected by at least 1/2 of the SSI runs. Besides the number of components in each cluster, the deviation of the components should also be examined because theoretically the eigenvalues representing the same mode should be close to each other for different model orders. In order to ensure that the deviations of the eigenvalues are within a certain range, v0, the threshold for the standard deviation, should be set to a relatively small value. (c) Calculate the cluster centre for dominant modes. For the results of Stepwise classification, the centre of each cluster and the oscillatory frequency and damping of each dominant mode are calculated via the following equations: (14a) (14b) (14c)where gi,j is the jth eigenvalue that belongs to cluster Gi; ni is the number of elements in cluster Gi; is the centre of cluster Gi; and are the oscillatory frequency and damping for the corresponding dominant mode, respectively. (d) Refine the clusters. Although the dominant modes can be estimated from the identified dominant mode clusters, the results are very sensitive to large prediction errors. Even when there is only a small number of outliers, the identified modes can be affected significantly and become unreliable due to the large prediction errors brought by these outliers. To overcome this issue, a cluster refinement is further developed in this proposed approach to reduce the negative influence of outliers. A cluster refinement function is defined as (15). If a component does not satisfy (15), this component is considered as an outlier and should be removed from the cluster, otherwise this component is retained (15)where , , and are the tolerances, respectively. In general, the setting of ςf and ςr can be guided by system operation experiences. (e) Calculate the dominant electromechanical oscillatory modes. Based on the refined clusters, the dominant electromechanical oscillatory modes and their oscillatory frequencies and damping are calculated via (14). 3.5 Flowchart of the proposed approach According to the approach mentioned above, the flowchart of the proposed approach for identifying dominant electromechanical modes is shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Flowchart of the proposed approach 4 Numerical examples In this section, CSG is used to test and evaluate the performance of the proposed approach. CSG is one of the largest AC/DC paralleling transmission systems in China, which includes Yunnan (YN), Guizhou (GZ), Guangxi (GX), Hainan (HN), and Guangdong (GD) provincial power grids. Electric energy is transmitted from the west (YN, GZ, and GX) to the demand centre in the east (GD) through corridors consist of five HVDC links and three AC corridors. Years of operation experiences suggest that there are two major inter-area oscillatory modes, YN–GD and YN–GZ oscillatory modes, which seriously limit the inter-area power transfer capacity. The YN–GD mode is at 0.30–0.43 Hz with 9.9–18.6% damping. While the YN–GZ mode is at 0.48–0.60 Hz with 9.5–15.8% damping [23]. Several control strategies have been implemented to enhance the small-signal stability of CSG including PSS, HVDC coordinated control and so on. Nevertheless, several recent notable oscillation incidences have brought the real-time monitoring of the inter-area oscillatory modes to the company's attention. As a consequence, PMUs have been extensively deployed in all the 500 kV substations and plants, and phasor data concentrator is installed in the dispatching and control centre of CSG. At 16:07:10.007 on 11 August 2012, a branch contingency occurred. Fig. 2 shows the recorded branch active powers obtained from PMUs at LP, LD, CZ, SZ, JZ, and SD captured this contingency. Fig. 2Open in figure viewerPowerPoint Recorded data of WAMS under a branch contingencya LP substationb LD substationc CZ substationd SZ substatione JZ substationf SD substation 4.1 Implementation of the proposed approach In this subsection, the ringdown data of active power transferred through YC branch between t = 15 s and t = 30 s (shown in Fig. 2c) is analysed in detail as an example to illustrate and verify the proposed approach. Step 1: Initial model order determination: DATA-SSI described in Section 2 is applied to the ringdown data of active power of YC. The projection weighting matrix W is solved with the model order set as rmax = 200. The singular values are shown in Fig. 3a. It is clearly illustrated that S in (8) is a full rank matrix and therefore the model order determination through SVD is unfeasible as discussed in Section 3.1. CCRSV defined in (12) is then calculated and depicted in Fig. 3b. With η0 set as 0.5 (red curve in Fig. 3b), it is found that k = 61 with ηk = 0.5017 is where CCRSV first meets η0. According to the criterion in (13), the initial model order is thus determined to be 62. Step 2: Initial cluster formation: SSI is carried out with repetitive calculations for model order r ranges from 62 to 200, with increment of 2 (i.e. p = 70). The initial cluster is shown in Fig. 4a. Step 3: HC: The proposed HC is applied to the initial cluster to discriminate the dominant modes. Here, with p = 70, N0 is set to be 35 (p/2). Moreover, referring to [21], v0 is set to be 0.03. The steps of HC are illustrated in Fig. 5 and the results in each step are depicted from Figs. 4b–f. Three dominant modes, corresponding to cluster G7, G14, and G16, are identified in the final step in Fig. 4g. Comparing the final step in Fig. 4g with the intermediate steps, it is clear that the proposed HC performs well in identifying the dominant modes from trivial ones. The results are shown in Table 1. Step 4: Final refinement: In Fig. 4g, there is still a small number of eigenvalues far away from the centres of the clusters. These eigenvalues, if included, can deviate the estimated damping from the true value and affect the accuracy of the identification. To resolve such issues, the components in each cluster need to be refined. Δf, Δr, and Δm of each component in clusters G7, G14, and G16 are calculated through (15) and are shown in Fig. 6. Referring to [21, 24], , , and in (15) are set to be 0.05, 0.06, and 0.05. With the tolerances set, components of clusters G4, G5, and G6 are refined according to (15). The final refined results are shown in Fig. 4h. Comparing Fig. 4g with Fig. 4h, the components of each cluster in Fig. 4h concentrate in a smaller region than Fig. 4g which means components within the refined cluster tend to have identical oscillation modes. By ruling out the components not close enough to the centres, the refined clusters are able to provide more accurate results. Step 5: Calculation of dominant oscillatory modes: The centres of the clusters in Fig. 4h are calculated via (14a), and their oscillatory frequencies and damping are obtained via (14b) and (14c). The results are summarised in Table 2. Based on the results, three dominant oscillatory modes, −0.1399 + 2.3064i, −0.6589 + 3.3058i, and −0.3070 + 5.1327i, are triggered by this contingency. Combining the results with operational experiences of CSG, it is inferred that, the −0.1399 + 2.3064i mode with 0.3671 Hz frequency and 6.0585% damping corresponds to the YN–GD mode; the −0.6584 + 3.3058i mode with 0.5261 Hz frequency and 19.5456% damping corresponds to the YN–GZ mode; while the −0.3070 + 5.1327i mode with 5.9703% damping corresponds to local oscillatory mode of YN (NWYN–SWYN mode). Since the damping of each oscillatory mode is greater than 5%, it can be concluded that the CSG has enough damping to ensure the small-signal stability under this contingency. Table 1. Results of third classification Cluster Number Std. Mode Frequency, Hz Damping, % G7 70 0.00002 −0.1411 + 2.3063i 0.3671 6.1083 G14 53 0.0241 −0.2966 + 5.1442i 0.8187 5.7556 G16 59 0.0300 −0.6731 + 3.3143i 0.5275 19.9033 Table 2. Results of dominant mode Cluster Number Mode Frequency, Hz Damping, % G7 69 −0.1399 + 2.3064i 0.3671 6.0585 G14 30 −0.3070 + 5.1327i 0.8169 5.9703 G16 28 −0.6589 + 3.3058i 0.5261 19.5456 Fig. 3Open in figure viewerPowerPoint Model order selectiona Singular values of SVDb CCRSV Fig. 4Open in figure viewerPowerPoint Diagram of the proposed HCa Initial clusterb First clusteringc Second clusteringd Third clusteringe Forth clusteringf Fifth clusteringg Retained clustersh Refined clusters Fig. 5Open in figure viewerPowerPoint Process of stepwise classification Fig. 6Open in figure viewerPowerPoint Deviations of each clustera Deviation of

Summary This paper proposes an adaptive formation reconfiguration control scheme based on the leader‐follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network–based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space–based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite‐time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision.

Oscillations threaten the stability of a power system. Timely detecting oscillations can improve operators' situational awareness of system stability and enable remedial reactions. To detect oscillations during their early stages, this paper proposes a cross-coherence method using multiple-channel phasor measurement unit (PMU) data. Oscillations are related to the peaks in coherence spectra and can be detected by visual inspection and setting up a threshold. Using simulation data, it is shown that the proposed coherence method can detect oscillations even under low signal-to-noise ratios. Three algorithms for estimating coherence spectra are implemented and evaluated. Their performances are compared using Monte Carlo methods. Based on the comparison, this paper makes some recommendations for proper use of the algorithms.

Continuous respiratory monitoring is extensively important in clinical applications. To effectively assess respiration rate (RR), tidal volume (TV), and minute ventilation (MV), we propose and experimentally demonstrate a respiration monitoring system using an in-line few-mode fiber Mach-Zehnder interferometer (FMF-MZI), which is the first to introduce in-line MZI into an optimal wearable design for respiration rate and volume monitoring. The optimal linear region of the proposed sensor is analyzed and positioned by a flexible arch structure with curvature sensitivity up to 8.53 dB/m −1 . Respiration monitoring results are in good agreement with a standard spirometer among different individuals. The difference in TV estimation is ± 0.2 L, and the overall error of MV estimation is less than 5%.

Small signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper develops a recursive algorithm for implementing Prony analysis and propose an oscillation detection method to detect ringdown data in real time. By automatically detecting ringdown data, the proposed method helps to guarantee that Prony analysis is properly and timely applied on the ringdown data. Thus, the mode estimation results can be performed reliably and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis.

Power system mode shapes are a key indication of how dynamic components participate in low-frequency oscillations. Traditionally, mode shapes are calculated from a linearized dynamic model. For large-scale power systems, obtaining accurate dynamic models is very difficult. Therefore, measurement-based mode shape estimation methods have certain advantages, especially for the application of real-time small signal stability monitoring. In this paper, a measurement-based mode shape identification method is proposed. The general relationship between transfer function (TF) and mode shape is derived. As an example, a least square (LS) method is implemented to estimate mode shape using an autoregressive exogenous (ARX) model. The performance of the proposed method is evaluated by Monte-Carlo studies using simulation data from a 17-machine model. The results indicate the validity of the proposed method in estimating mode shapes with reasonably good accuracy.

A robust model for power system load forecasting covering different horizons of time from short-term to long-term is an indispensable tool to have a better management of the system. However, as the horizon of time in load forecasting increases, it will be more challenging to have an accurate forecast. Machine learning methods have got more attention as efficient methods in dealing with the stochastic load pattern and resulting in accurate forecasting. In this study, the problem of long-term load forecasting for the case study of New England Network is studied using several commonly used machine learning methods such as feedforward artificial neural network, support vector machine, recurrent neural network, generalized regression neural network, k-nearest neighbors, and Gaussian Process Regression. The results of these methods are compared with mean absolute percentage error (MAPE).

In obstacle environments, the problem of coordination tracking control for a team of Euler–Lagrange systems is investigated under modeling uncertainties, actuator faults and disturbances. First, syncretizing the Null-Space-based Behavioral (NSB) control, graph theory and finite-time control method, a novel desired velocity is predesigned to achieve the finite-time obstacle avoidance and coordination tracking. Then a set of finite-time fault-tolerant coordination control laws (FFCCLs) are presented to guarantee all of the agents to track a dynamic target while avoiding obstacles/collisions. To improve the robustness and control accuracy of the systems, an adaptive control gain is incorporated into the FFCCL so that the derived algorithm can be implemented without manual parameter adjustment. Both of the control architectures are distributed, model-independent and robust with respect to modeling uncertainties, actuator faults and disturbances. Finally, several numerical simulations are presented to demonstrate the efficacy of the control strategies, showing that the overall motion of the two tasks can be accomplished satisfactorily with high precision.

The steady-state security region of the integrated energy system (IES) is a useful tool for rapid security assessment and security evaluation of the integrated energy system. A complete characterization of the IES steady-state security region is derived. The derivation is under the nonlinear and non-convex AC power flow model, the gas flow model, and the uncertainty of renewable energy without any linear simplification. Then a novel and robust computation scheme to compute the IES steady-state security region is proposed and shown to eliminate the estimation errors of the existing methods. It is shown that the IES steady-state security region is non-convex and composed of several disjoint components. Numerical studies are conducted to show that the IES steady-state security regions obtained from the existing methods can be inaccurate, while the proposed characterization gives an exact estimation of the IES steady-state security region that significantly improves the previously proposed methods.

In this article, we investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents, aiming to achieve a desired formation with collision/obstacle avoidance. In the proposed approach, the two behaviors(tasks) for each agent are prioritized and integrated via the framework of the null-space-based behavioral projection, leading to a desired merged velocity that guarantees the fixed-time convergence of task errors. To track this desired velocity, we design a fixed-time sliding-mode controller for each agent with state-independent adaptive gains, which provides a fixed-time convergence of the tracking error. The control scheme is implemented in a distributed manner, where each agent only acquires information from its neighbors in the network. Moreover, we adopt an online learning algorithm to improve the robustness of the closed system with respect to uncertainties/disturbances. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Respiratory oscillometry is a promising complement to the traditional pulmonary function tests for its simplicity. The usefulness of oscillometry in adult clinical practice has not been clarified. This study aimed to analyse the characteristics and diagnostic performance of oscillometry in respiratory diseases, and explore the cut-offs of oscillometric parameters for severity grading.In this multicentre registry of impulse oscillometry (IOS), IOS and spirometric data of healthy individuals and patients with respiratory diseases were collected and analysed. Linear mixed model analysis was performed to explore the effects of disease and forced expiratory volume in 1 s (FEV1) on oscillometric parameters.The study included 567 healthy subjects, 781 asthmatic patients, 688 patients with chronic obstructive pulmonary disease (COPD), 109 patients with bronchiectasis, 40 patients with upper airway obstruction (UAO) and 274 patients with interstitial lung disease (ILD) in the analysis. Compared at the same FEV1 level, asthma, COPD, bronchiectasis, UAO and ILD displayed different oscillometric characteristics. The z-score of resistance at 5 Hz (R5) was the best variable to identify respiratory diseases with a sensitivity of 62.4-66.7% and a specificity of 81.5-90.3%. With reference to the severity grading cut-offs of FEV1, R5 z-scores of 2.5 and 4 were defined as the cut-off values of moderately and severely increased R5.Respiratory oscillometry is more appropriate to be a tool of evaluating, rather than of diagnosing, respiratory diseases. A severity grading system of oscillometric parameters was developed to help the interpretation of oscillometry in clinical practice.

ModeMeter techniques for real-time small-signal stability monitoring continue to mature, and more and more phasor measurements are available in power systems. It has come to the stage to bring modal information into real-time power system operation. This paper proposes to establish a procedure for Modal Analysis for Grid Operations (MANGO). Complementary to PSS and other traditional modulation-based control, MANGO aims to provide suggestions such as redispatching generation for operators to mitigate low-frequency oscillations. Load would normally not be reduced except as a last resort. Different from modulation-based control, the MANGO procedure proactively maintains adequate damping at all times, rather than reacting to disturbances when they occur. The effect of operating points on small-signal stability is presented in this paper. Implementation with existing operating procedures is discussed. Several approaches for modal sensitivity estimation are investigated to associate modal damping and operating parameters. The effectiveness of the MANGO procedure is confirmed through simulation studies of several test systems.

The problem of finite-time attitude synchronisation and tracking for a group of rigid spacecraft nonlinear dynamics is investigated in this paper. First of all, in the presence of environmental disturbance, a novel decentralised control law is proposed to ensure that the spacecraft attitude error dynamics can converge to the sliding surface in finite time; then the final practical finite-time stability of the attitude error dynamics can be guaranteed in small regions. Furthermore, a modified finite-time control law is proposed to address the control chattering. The control law can guarantee a group of spacecraft to attain desired time-varying attitude and angular velocity while maintaining attitude synchronisation with other spacecraft in the formation. Simulation examples are provided to illustrate the feasibility of the control algorithm presented in this paper.

Accurate estimation of dynamic states is important for monitoring and controlling transient stability. This paper proposes an adaptive interpolation approach to improve the performance of the Extended Kalman Filter (EKF) for estimating dynamic states of a synchronous machine. This approach consists of two major steps. First, the non-linearity of state transition function and measurement function is quantified. Second, when the non-linearity is severe, pseudo measurements are added through interpolation to mitigate the negative impact of non-linearity on the estimation accuracy. Using the 2-area model, it is shown that the proposed adaptive interpolation approach can make a good tradeoff between estimation accuracy and computation time when estimating the dynamic states of a synchronous machine.

To select optimal locations for placing phasor measurement units (PMU), in this paper, a two-step convex optimization approach to attain the full network observability of a power system in both the normal operation conditions and controlled islanding operation conditions is proposed. The objective is to get the minimum number of PMUs which guarantees the system full observability with the maximum measurement redundancy. In the first step, a minimization model is applied to convex programing (cvx) to determine the minimum number of PMUs and all the possible candidate buses, which ensures the full network observability. In the second step, in the cases of multiple solutions, a maximization model is applied to cvx to maximize the measurement redundancy. Furthermore, the effect of zero-injection buses is considered to further reduce the number of required PMUs. The proposed approach is tested on three IEEE test systems, i.e. IEEE 14-bus, 30-bus and 118-bus, to demonstrate its effectiveness.

This paper proposes a new objective function and quantile regression (QR) algorithm for load forecasting (LF). In LF, the positive forecasting errors often have different economic impact from the negative forecasting errors. Considering this difference, a new objective function is proposed to put different prices on the positive and negative forecasting errors. QR is used to find the optimal solution of the proposed objective function. Using normalized net energy load of New England network, the proposed method is compared with a time series method, the artificial neural network method, and the support vector machine method. The simulation results show that the proposed method is more effective in reducing the economic cost of the LF errors than the other three methods.

An online line switching methodology to alleviate line overloads with look-ahead capability is proposed in this paper. This novel online methodology is based on a three-stage strategy, including screening, ranking, and detailed analysis and assessment stages for fast speed (online application) and accuracy. The proposed online methodology performs the tasks of rapidly identifying effective candidate lines, ranking the effective candidates, detailed analysis of the top ranked candidates, and supplying a set of effective line switching solutions for a current operating point. Both the current and the look-ahead post-switching power systems, after executing the proposed line switching action, meet the operational and engineering constraints. One distinguishing feature of the proposed methodology is that it provides a set of high-quality line switching solutions. The results provided by the exact AC power flow are used as a benchmark to compare the accuracy of the proposed three-stage methodology. The effectiveness and fast speed of the proposed line switching methodology with look-ahead capability are evaluated on the IEEE 39-bus and 2746-bus power systems with promising results.

Abstract We present a systematic determination of the responses of PandaX-II, a dual phase xenon time projection chamber detector, to low energy recoils. The electron recoil (ER) and nuclear recoil (NR) responses are calibrated, respectively, with injected tritiated methane or 220 Rn source, and with 241 Am-Be neutron source, in an energy range from 1-25 keV (ER) and 4-80 keV (NR), under the two drift fields, 400 and 317 V/cm. An empirical model is used to fit the light yield and charge yield for both types of recoils. The best fit models can describe the calibration data significantly. The systematic uncertainties of the fitted models are obtained via statistical comparison to the data.

Compared with the signature of dark matter elastic scattering off nuclei, the absorption of fermionic dark matter by nuclei opens up a new searching channel for light dark matter with a characteristic monoenergetic signal. In this Letter, we explore the 95.0-day data from the PandaX-4T commissioning run and report the first dedicated searching results of the fermionic dark matter absorption signal through a neutral current process. No significant signal was found, and the lowest limit on the dark matter-nucleon interaction cross section is set to be 1.5×10^{-50} cm^{2} for a fermionic dark matter mass of 40 MeV/c^{2} with 90% confidence level.

Phosphorescent cationic tridentate C^N^N (HC^N^N = 6-(2-R2,4-R1-phenyl)-2,2'-bipyridine; R1 = R2 = H or F, or R1 = OMe, R2 = H) cyclometallated Pd(II) complexes with an N,N-dimethyl-imidazol-allenylidene ancillary ligand (L) and their corresponding Pt(II) congeners have been synthesized and characterized, following the previously reported preparation of the complex [Pd(6-phenyl-2,2'-bipyridine)L]+. For these cationic Pd(II)/Pt(II) complexes with 2,3,4-tris(dodecyloxy)benzenesulfonate (LA-) counter-anions in mixed CH2Cl2/toluene solvents, uniform square flake or fibre-like aggregates were formed. Corresponding multicolour phosphorescence with obvious metal-metal-to-ligand charge transfer (MMLCT) features gradually changed from red to NIR by manipulating the various fractions of Pd/Pt species. Circular dichroism (CD) and circularly polarized luminescence (CPL) derived from the fibre-like Pd aggregates of [Pd(6-(2,4-difluorophenyl)-2,2'-bipyridine)L]+ in chiroptical CH2Cl2/limonene solvents were achieved with an isodesmic aggregation mode. Dispersive metallophilic interactions are claimed to be the driving force for these photo-functional aggregates.

Abstract The PandaX-30T is a proposed next-generation experiment to study dark matter and neutrinos using a dual-phase time projection chamber with ∼ 30 tons of liquid xenon. An innovative xenon handling subsystem of the PandaX-30T, the First-X, is described in this paper. The First-X is developed to handle liquid xenon safely and efficiently, including liquefying and long-term storing xenon without losses or contamination, and transferring cryogenic liquid xenon between the storage module and the detector safely and effectively without venting out. The storage module of the First-X is five specially designed double-walled cylindrical vessels (Center Tanks) equipped with three heat exchangers each for pressure and temperature regulation. Each Center Tank is designed with a vacuum and multi-layer insulation and a maximum allowable working pressure of 7.1 MPa, allowing 6 tons of xenon to be stored at 165–178 K at 0.1–0.2 MPa in the liquid phase or up to 300 K and up to 6.95 MPa in the supercritical phase. High-pressure storage (&gt; 0.2 MPa) only occurs in case of long-term detector shutdown or lack of nitrogen, ensuring no-loss storage of 6 tons of xenon in the range 178–300 K. In this paper, the thermophysical performances of the First-X and innovative scenario to conduct non-vented cryogen transportation were experimentally conducted and reported using liquid argon. The non-vented cryogenic liquid filling and pump-assisted cryogenic liquid recovery have been conducted with liquid argon at a mass flow rate of 1390 kg/h, corresponding to a xenon mass flow rate of 2140 kg/h. Compared with the PandaX-4T, the transportation of xenon between the detector and the storage module is conducted in the liquid phase rather than in the gaseous phase, and the filling rate (fill the detector) and the recovery rate (recover xenon from the detector) are increased by approximately 50 times and 30 times, respectively.

In this paper, a coherence method is proposed to detect and analyze forced oscillations from phasor measurement unit data. The calculation of coherence spectrum is reviewed, and forced oscillations are related to the peaks in the coherence spectrum. Using simulation and field measurement data, the proposed coherence method's performance is compared with the power spectral density method. When applied to detect and analyze forced oscillations under low signal noise ratio conditions, the proposed coherence method shows better performance.

Bounds on invisible decays of the Higgs boson from $t\bar{t}H$ production were inferred from a CMS search for stop quarks decaying to $t\bar{t}$ and missing transverse momentum. Limits on the production of $t\bar{t}H$ relied on the efficiency of the CMS selection for $t\bar{t}H$, as measured in a simulated sample. An error in the generation of the simulated sample lead to a significant overestimate of the selection efficiency. Corrected results are presented.

In order to effectively implement remedial reactions to mitigate the negative impacts of oscillations on a power grid, it is essential for system operators to timely and accurately determine whether an observed oscillation is a natural oscillation or a forced oscillation. Using phasor measurement unit (PMU) data, this paper proposes a residual spectral analysis (RSA) method to distinguish forced oscillations and natural oscillations. The proposed RSA method uses forecasting models with various lead times to forecast the current PMU data based on the past PMU data. The spectra of the forecasting residuals are shown to have different properties when the PMU data have forced or natural oscillations. Support vector machines are applied to the residuals to classify the oscillations. This paper develops an algorithm for implementing the RSA method, and demonstrates its superior performance via extensive simulations over the 48-machine model. Simulations show that it can distinguish the oscillations more accurately than an existing method and work reliably even when the frequency of forced oscillations is close to that of natural oscillations.

Distributed sensors are the eyes and ears of a smart grid which provide information vital for monitoring and controlling the entire power generation, transmission, and distribution systems. Secure exchange of information among the sensing and decision-making entities is essential as failures may bring the entire system on its knees. With the rapid growth in the number of distributed sensors, drones have a myriad of applications. A swarm of drones could also be deployed in war zones and disaster-stricken areas where a secured intercommunication is of paramount importance for survivability and for successful mission completion. In this paper, a secure mechanism is proposed based on mobile agents to secure information exchange with minimum overhead. An Agent Administrator (AA) automatically clones and sends a secure mobile agent (SMA) to the target sensors or drones to scan and check their security status. Then, the dispatched SMAs send feedbacks to the server AA or other members. In case of sensors, the closest terminal unit to which the sensors are directly connected is designated as an AA, which is capable of checking authentication and scanning for vulnerabilities. In the case of drones, any one of them or multiple of them could be designated as the AA and the flagged feedback is broadcast to all other nodes or drones thereby providing them security status updates. A modified Nagle’s Algorithm is also proposed to support real-time video transmission. The experimental results validate the effectiveness and convenience of the proposed system.

We report the Neutrino-less Double Beta Decay (NLDBD) search results from PandaX-II dual-phase liquid xenon time projection chamber. The total live time used in this analysis is 403.1 days from June 2016 to August 2018. With NLDBD-optimized event selection criteria, we obtain a fiducial mass of 219 kg of natural xenon. The accumulated xenon exposure is 242 kg$\cdot$yr, or equivalently 22.2 kg$\cdot$yr of $^{136}$Xe exposure. At the region around $^{136}$Xe decay Q-value of 2458 keV, the energy resolution of PandaX-II is 4.2%. We find no evidence of NLDBD in PandaX-II and establish a lower limit for decay half-life of 2.4 $ \times 10^{23} $ yr at the 90% confidence level, which corresponds to an effective Majorana neutrino mass $m_{\beta \beta} < (1.3 - 3.5)$ eV. This is the first NLDBD result reported from a dual-phase xenon experiment.

<h3>Abstract</h3> Filtering-based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self-contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao-Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle-of-Arrival measurements in particle-filter-based positioning improves the 3-D positioning accuracy by about 37.3% on average.

Self-interacting Dark Matter (SIDM) is a leading candidate proposed to solve discrepancies between predictions of the prevailing cold dark matter theory and observations of galaxies. Many SIDM models predict the existence of a light force carrier that mediate strong dark matter self-interactions. If the mediator couples to the standard model particles, it could produce characteristic signals in dark matter direct detection experiments. We report searches for SIDM models with a light mediator using the full dataset of the PandaX-II experiment, based on a total exposure of 132 tonne-days. No significant excess over background is found, and our likelihood analysis leads to a strong upper limit on the dark matter-nucleon coupling strength. We further combine the PandaX-II constraints and those from observations of the light element abundances in the early universe, and show that direct detection and cosmological probes can provide complementary constraints on dark matter models with a light mediator.

Abnormal peripheral perfusion and postinduction hypotension are associated with postoperative adverse outcomes. S-ketamine may stimulate blood circulation by activating the sympathetic nervous system. This study aimed to identify whether S-ketamine may improve the hemodynamic profile, relative to saline. 115 patients were assessed for eligibility for participation in this study. A total of 100 patients were included. The patients (n = 50 for each group) were randomly allocated to the Test group, treated with S-ketamine plus propofol, cisatracurium and sufentanil, and to the Control group, treated with saline plus propofol, cisatracurium, and sufentanil. Maintenance of anesthesia in both groups was accomplished with sevoflurane. The perfusion index (PI) was recorded at intervals of 1 min, and mean arterial pressure (MAP) and heart rate (HR) were collected continuously at intervals of 3 min. The number of patients with MAP < 60 mmHg was also analyzed. The PI remained higher in the Test group than in the Control group at the following time points: initial induction (mean difference: 1.01 [95% CI: 0.27–1.74]; P = 0.007), preintubation (mean difference: 1.46 [95% CI: 0.57–2.34]; P = 0.001) and postintubation (mean difference: 1.28 [95% CI: 0.26–2.30]; P = 0.014), before (mean difference: 2.66 [95% CI: 1.22–4.1]; P < 0.001) and after making the skin incision (mean difference: 1.03 [95% CI: 0.28–1.78]; P = 0.007). Compared with Control group, a higher MAP trend from preintubation to postincision appeared in patients assigned to S-ketamine (P = 0.003). The number of patients with MAP < 60 mmHg in the Test group was lower than Control group (10.0% vs. 34.0%, P < 0.003) in the preincision. The HR was similar throughout the test, with no statistical difference. During anesthesia induction and maintenance, the use of S-ketamine may improve the peripheral perfusion and blood pressure as compared to the Control group. ChiCTR2100051167.

The study investigates the symmetric and asymmetric impact of agriculturalization on CO2 emissions in a sample of selected Asian economies for time period 1985 to 2019. For empirical analysis, the study adopted panel linear and nonlinear autoregressive distributed lag (ARDL) approaches. The long-run findings of panel ARDL reveal that agriculturalization contributes to environmental quality by mitigating CO2 emissions. The panel nonlinear results clearly indicate that the effects of agriculturalization on CO2 emissions are asymmetric. The findings demonstrate that agriculturalization improves environmental quality and de-agriculturalization mitigates environmental quality. Our empirical results are also robust to alternative model specifications. Based on these findings, the study recommends that the relevant authorities should formulate reforms in the agriculture sector that controls and reduces carbon emissions in Asian economies.

In large-scale ground source heat pump (GSHP) systems, the accumulation of thermal energy resulting from seasonal load imbalances has emerged as a significant impediment. The implementation of a rational operation strategy can effectively mitigate heat accumulation in soil. This study investigates the impact of the configurational operation strategy for large-scale vertical ground heat exchanger (GHE) on the spatial distribution of ground temperature under the influence of groundwater flow. The internal heat source model is proposed to describe heat transfer process of GHE. Sandbox experiment is conducted to verify the model with maximum deviation of 3 %. The validated model is then utilized to simulate soil temperature profile in the large-scale GHE field over a 10-year operational period, considering a matrix of three configuration strategies and three operation strategies. The results shows that trapezoidal configurations combined with operation Strategy 3 has a minimal temperature increment of 2.9 °C in soil temperature. The contribution of this study lies in provision of a methodology for mitigating thermal accumulation in large-scale GHE field.

Abstract Background and objective: Subjective well-being stands as a pivotal and comprehensive psychological indicator reflecting an individual's quality of life, often intertwined with physical activity. However, the intricate mechanisms through which physical activity influences an individual's subjective well-being remain less explored. Currently, scant research delves into the impact of physical activity on subjective well-being concerning cardiac dimensions such as basic psychological needs and self-efficacy. Consequently, this study aims to investigate the influence of physical activity on the subjective well-being of college students, examining the chain-mediated effects of basic psychological needs and self-efficacy. Methods: A total of 389 college students participated in this study, and data collection involved utilizing the Physical Activity Rating Scale, Subjective Well-Being Scale, Basic Psychological Needs Scale, and Self-Efficacy Scale. Statistical analysis was conducted using SPSS 24.0 to explore the chain mediating roles of basic psychological needs and self-efficacy in the effects of physical activity on college students' subjective well-being. Results: The findings revealed a significant positive correlation among physical activity, subjective well-being, basic psychological needs, and self-efficacy. Notably, physical activity influences college students' subjective well-being through the mediating roles of basic psychological needs and self-efficacy, with each playing a chain mediating role independently. Conclusion: Basic psychological needs and self-efficacy as integral chain mediators in elucidating the effects of physical activity on the subjective well-being of college students.

In this brief, a disturbance observer based prescribed-time tracking control strategy is proposed for a class of high-order nonlinear systems with non-vanishing uncertainties. A finite time function is firstly introduced to facilitate the disturbance observer and controller design. Then, a prescribed-time disturbance observer is constructed to estimate and compensate for the non-vanishing uncertainties, which ensures that the estimation error converges to zero in a prescribed time. Based on the backstepping design framework, the prescribed-time controller is further designed in the form of scaling the tracking error. Theoretical analysis reveals that the proposed controller achieves the excellent prescribed-time convergence of the full state tracking errors, and the convergence time can be explicitly predetermined, independent of the initial conditions. Finally, the validity and superiority of the proposed controller is demonstrated via numerical simulation.

Unmanned aerial vehicles (UAVs) operating as airborne base stations (UAV-BSs) provide efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile, allowing them to be strategically deployed based on ground user distribution and quality of service requirements, including coverage rate, system lifecycle, and user fairness. Owing to the limited battery capacity and coverage range of the UAVs, managing them to extend their operational lifecycle, ensure service fairness, and maintain a specific real-time coverage rate is challenging. Therefore, a multi-objective optimization problem with constrained Pareto dominance is formulated. Subsequently, a novel assisted deep reinforcement learning model is developed to maximize the minimum remaining energy while simultaneously considering user fairness and coverage-rate requirements. The particle swarm optimization algorithm is adopted to assist multi-agent cooperative deep reinforcement learning. Finally, the simulation results show that the proposed model outperforms the other popular methods in terms of user fairness, system lifecycle, coverage rate, and energy efficiency in the context of multi-objective, multi-agent cooperative coverage control deployment.

Image recognition and classification have found extensive applications in the field of artificial intelligence. This study conducts a comparative study of conventional Convolutional Neural Network (CNN) and the Visual Geometry Group Network (VGG16) to explore the effectiveness of model classification. The aim is to comprehensively understand the interactions among different hierarchical image feature representations in scenery classification tasks, the models' performance in complex feature learning, and non-linear expression capabilities. The experiments conducted on the "Intel Image Classification" dataset demonstrate the superior accuracy of the pre-trained VGG16 model over the CNNs model, particularly in terms of feature learning and generalization capabilities. Moreover, this comparative analysis approach enhances the understanding of the characteristics and suitable scenarios of different network architectures. It guides the selection, design, and optimization of deep learning models for practical image classification applications. Therefore, the study makes a significant contribution to the field of scene image classification and offers practical implications. It establishes a foundation for future research directions that will emphasize innovation in models, integration of multimodal data, and improvement in robustness and interpretability, aiming to drive breakthroughs and applications of image recognition and classification technology across various fields.

Abstract This article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller.

When analyzing the electromechanical dynamic properties of power-system field-measurement data using signal processing techniques, it is often useful to identify and remove the slow trends within the data. This paper proposes an iterative non-linear trend identification algorithm. The proposed method adapts the upper and lower envelope idea proposed by empirical mode decomposition (EMD) method to identify the trend. The comparison with conventional trend identification methods are made with simulation data. Also, the proposed algorithm is applied to a field measurement data set to evaluate its performance.

With the rapidly growing penetration level of wind and solar generation, the challenges of managing variability and the uncertainty of intermittent renewable generation become more and more significant. The problem of power variability and uncertainty gets exacerbated when each balancing authority (BA) works locally and separately to balance its own subsystem. The virtual BA concept means various forms of collaboration between individual BAs must manage power variability and uncertainty. The virtual BA will have a wide area control capability in managing its operational balancing requirements in different time frames. This coordination results in the improvement of efficiency and reliability of power system operation while facilitating the high level integration of green, intermittent energy resources. Several strategies for virtual BA implementation, such as ACE diversity interchange (ADI), wind only BA, BA consolidation, dynamic scheduling, regulation and load following sharing, extreme event impact study are discussed in this report. The objective of such strategies is to allow individual BAs within a large power grid to help each other deal with power variability. Innovative methods have been developed to simulate the balancing operation of BAs. These methods evaluate the BA operation through a number of metrics — such as capacity, ramp rate, ramp duration, energy and cycling requirements — to evaluate the performances of different virtual BA strategies. The report builds a systematic framework for evaluating BA consolidation and coordination. Results for case studies show that significant economic and reliability benefits can be gained. The merits and limitation of each virtual BA strategy are investigated. The report provides guidelines for the power industry to evaluate the coordination or consolidation method. The application of the developed strategies in cooperation with several regional BAs is in progress for several off-spring projects.

Large interconnected power systems are arguably some of the most complicated man-made systems to understand and to characterize. The scale of the problem is immense, involving large numbers of generators, controllers, and transmission lines covering millions of square kilometers. Measurement technology has reached a point where Phasor Measurement Units (PMUs) are being widely installed in power systems all over the world. These devices provide time synchronized (via GPS) phasor measurements from throughout the power grid to Phasor Data Concentrators (PDCs) at power system control centers. These time series can be used to better characterize the system and hopefully, in the long term, to better control the system. This paper presents a tutorial on estimating power system characteristics from measured responses. About a given operating point, power system low-frequency dynamics are well modeled as a high-order, multi-input, multi-output linear system. Of primary interest is the estimation of the inter-area electromechanical modes of the system. These inter-area modes involve generators from one area of the system oscillating against generators in another area of the system. The modes are characterized by their frequency, damping, and shape. In August 1996, the western United States experienced a massive wide spread black out caused by an unstable inter-area mode, involving generators in the north swinging against generators in the south. This paper overviews the problem and examines several methods of estimating the electromechanical modes under different signal conditions. Several real-world examples are given for estimating the electromechanical modes from ambient, transient, or probing situations. When the system is probed, more general state-space and transfer-function models are estimated. Probing a power system with known inputs is challenging and is discussed in this paper. Estimation performance issues are also discussed.

The study of collision events with missing energy as searches for the dark matter (DM) component of the Universe are an essential part of the extensive program looking for new physics at the LHC. Given the unknown nature of DM, the interpretation of such searches should be made broad and inclusive. This report reviews the usage of simplified models in the interpretation of missing energy searches. We begin with a brief discussion of the utility and limitation of the effective field theory approach to this problem. The bulk of the report is then devoted to several different simplified models and their signatures, including s-channel and t-channel processes. A common feature of simplified models for DM is the presence of additional particles that mediate the interactions between the Standard Model and the particle that makes up DM. We consider these in detail and emphasize the importance of their inclusion as final states in any coherent interpretation. We also review some of the experimental progress in the field, new signatures, and other aspects of the searches themselves. We conclude with comments and recommendations regarding the use of simplified models in Run-II of the LHC.

Summary In this paper, a motion control problem of multi‐agent systems for escorting a target is investigated by employing nonsingular fast terminal sliding mode control and adaptive control associated with kinematic control. The proposed control law is robust to model uncertainty and disturbances, and it guarantees all the agents to scatter around the target evenly and escort it with a fixed distance while avoiding obstacles (or collisions) in p ‐dimensional case ( is a positive integer). Finite‐time convergence of the position errors and velocity errors is proved rigorously by a Lyapunov‐based approach and finite‐time control techniques. Simulation results in both two‐dimensional and three‐dimensional space are provided to illustrate the effectiveness and high‐precision performance of the control algorithm compared with the traditional adaptive sliding mode control, showing that all the agents can move into suitable positions on the surface of the sphere in the escort mission, and the formation can be reconfigured automatically when the obstacle (or collision) avoidance task is active. Copyright © 2014 John Wiley &amp; Sons, Ltd.

With the increasing complexity resulting from uncertainties and stochastic variations introduced by intermittent renewable energy sources, responsive loads, mobile consumption of plug-in vehicles, and new market designs, more and more dynamic behaviors are observed in everyday power system operation. To operate a power system efficiently and reliably, it is critical to adopt a dynamic paradigm so that effective control actions can be taken in time. The dynamic paradigm needs to include three fundamental components: dynamic state estimation; look-ahead dynamic simulation; and dynamic contingency analysis (Figure 1). These three components answer three basic questions: where the system is; where the system is going; and how secure the system is against accidents. The dynamic state estimation provides a solid cornerstone to support the other 2 components and is the focus of this study.

The power grid evolves towards a new mix of generation and consumption that introduces new dynamic and stochastic behaviors. These emerging grid behaviors would invalidate the steady-state assumption in today's state estimation - an essential function for real-time power grid operation. This paper examines this steady-state assumption and identifies the need for estimating dynamic states. Supporting technologies are presented as well as a proposed formulation for estimating dynamic states. Metrics for evaluating methods for solving the dynamic state estimation problem are proposed, with example results to illustrate the use of these metrics. The overall objective of this paper is to provide a basis that more research on this topic can follow.

Decentralized attitude synchronization and tracking control for multiple rigid bodies are investigated in this paper. In the presence of inertia uncertainties and environmental disturbances, we propose a class of decentralized adaptive sliding mode control laws. An adaptive control strategy is adopted to reject the uncertainties and disturbances. Using the Lyapunov approach and graph theory, it is shown that the control laws can guarantee a group of rigid bodies to track the desired time-varying attitude and angular velocity while maintaining attitude synchronization with other rigid bodies in the formation. Simulation examples are provided to illustrate the feasibility and advantage of the control algorithm.

We study the Higgs boson $(h)$ decay to two light jets at the 14 TeV High-Luminosity-LHC (HL-LHC), where a light jet ($j$) represents any non-flavor tagged jet from the observational point of view. The decay mode $h\to gg$ is chosen as the benchmark since it is the dominant channel in the Standard Model (SM), but the bound obtained is also applicable to the light quarks $(j=u,d,s)$. We estimate the achievable bounds on the decay branching fractions through the associated production $Vh\ (V=W^\pm,Z)$. Events of the Higgs boson decaying into heavy (tagged) or light (un-tagged) jets are correlatively analyzed. We find that with 3000 fb$^{-1}$ data at the HL-LHC, we should expect approximately $1\sigma$ statistical significance on the SM $Vh(gg)$ signal in this channel. This corresponds to a reachable upper bound ${\rm BR}(h\to jj) \leq 4~ {\rm BR}^{SM}(h\to gg)$ at $95\%$ confidence level. A consistency fit also leads to an upper bound ${\rm BR}(h\to cc) < 15~ {\rm BR}^{SM}(h\to cc)$ at $95\%$ confidence level. The estimated bound may be further strengthened by adopting multiple variable analyses, or adding other production channels.