The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Xiaozhe Wang - One of the best experts on this subject based on the ideXlab platform.
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measurement based estimation of system State Matrix for ac power systems with integrated vscs
2020 IEEE Conference on Control Technology and Applications (CCTA), 2020Co-Authors: Jinpeng Guo, Xiaozhe Wang, Boonteck OoiAbstract:In this paper, a wide-area measurement system (WAMS)-based method is proposed to estimate the system State Matrix for AC system with integrated voltage source converters (VSCs) and identify the electromechanical modes. The proposed method is purely model-free, requiring no knowledge of accurate network topology and system parameters. Numerical studies in the IEEE 68-bus system with integrated VSCs show that the proposed measurement-based method can accurately identify the electromechanical modes and estimate the damping ratios, the mode shapes, and the participation factors. The work may serve as a basis for developing WAMS-based damping control using VSCs in the future.
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online measurement based estimation of dynamic system State Matrix in ambient conditions
IEEE Transactions on Smart Grid, 2020Co-Authors: Hao Sheng, Xiaozhe WangAbstract:In this paper, a purely measurement-based method is proposed to estimate the dynamic system State Matrix by applying the regression theorem of the multivariate Ornstein-Uhlenbeck process. The proposed method employs a recursive algorithm to minimize the required computational effort, making it applicable to the real-time environment. One main advantage of the proposed method is model independence, i.e., it is independent of the network model and the dynamic model of generators. Among various applications of the estimated Matrix, detecting and locating unexpected network topology change is illustrated in details. Simulation studies have shown that the proposed measurement-based method can provide an accurate and efficient estimation of the dynamic system State Matrix under the occurrence of unexpected topology change. Besides, various implementation conditions are tested to show that the proposed method can provide accurate approximation despite measurement noise, missing phasor measurement units (PMUs), and the implementation of higher-order generator models with control devices.
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CCTA - Measurement-Based Estimation of System State Matrix for AC Power Systems with Integrated VSCs
2020 IEEE Conference on Control Technology and Applications (CCTA), 2020Co-Authors: Jinpeng Guo, Xiaozhe Wang, Boonteck OoiAbstract:In this paper, a wide-area measurement system (WAMS)-based method is proposed to estimate the system State Matrix for AC system with integrated voltage source converters (VSCs) and identify the electromechanical modes. The proposed method is purely model-free, requiring no knowledge of accurate network topology and system parameters. Numerical studies in the IEEE 68-bus system with integrated VSCs show that the proposed measurement-based method can accurately identify the electromechanical modes and estimate the damping ratios, the mode shapes, and the participation factors. The work may serve as a basis for developing WAMS-based damping control using VSCs in the future.
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online measurement based estimation of dynamic system State Matrix in ambient conditions
arXiv: Signal Processing, 2019Co-Authors: Hao Sheng, Xiaozhe WangAbstract:In this paper, a purely measurement-based method is proposed to estimate the dynamic system State Matrix by applying the regression theorem of the multivariate Ornstein-Uhlenbeck process. The proposed method employs a recursive algorithm to minimize the required computational effort, making it applicable to the real-time environment. One main advantage of the proposed method is model independence, i.e., it is independent of the network model and the dynamic model of generators. Among various applications of the estimated Matrix, detecting and locating unexpected network topology change is illustrated in details. Simulation studies have shown that the proposed measurement-based method can provide an accurate and efficient estimation of the dynamic system State Matrix under the occurrence of unexpected topology change. Besides, various implementation conditions are tested to show that the proposed method can provide accurate approximation despite measurement noise, missing PMUs, and the implementation of higher-order generator models with control devices.
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pmu based estimation of dynamic State jacobian Matrix and dynamic system State Matrix in ambient conditions
IEEE Transactions on Power Systems, 2018Co-Authors: Xiaozhe Wang, J W Bialek, Konstantin TuritsynAbstract:In this paper, a hybrid measurement- and model-based method is proposed which can estimate the dynamic State Jacobian Matrix and the dynamic system State Matrix in near real time utilizing statistical properties extracted from PMU measurements. The proposed method can be used to detect and identify network topology changes that have not been reflected in an assumed network model. Additionally, an application of the estimated system State Matrix in online dynamic stability monitoring is presented.
Chun-hsiung Fang - One of the best experts on this subject based on the ideXlab platform.
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STABILITY ROBUSTNESS ANALYSIS OF UNCERTAIN DISCRETE-TIME DESCRIPTOR SYSTEMS
IFAC Proceedings Volumes, 2002Co-Authors: Chun-hsiung Fang, Li LeeAbstract:Abstract The stability robustness problem of uncertain discrete-time descriptor systems is investigated. Uncertainties appear not only in the State Matrix but also in the advanced State Matrix. Through checking the feasibility of a strict linear Matrix inequality problem, the robust stability of the uncertain system can be easily determined. The proposed result also gives a sufficient condition for the existence of a parameter-dependent Lyapunov function for such uncertain systems.
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Exact bounds for robust stability of generalized State-space systems with perturbations on derivative State Matrix
Proceedings of 35th IEEE Conference on Decision and Control, 1Co-Authors: Li Lee, Chun-hsiung FangAbstract:The stability robustness of uncertain generalized State-space systems with unidirectional perturbations on derivative State Matrix is investigated. The exact bound can be easily obtained by only checking the stability of some finite real points.
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Stability robustness analysis of uncertain descriptor systems - an LMI approach
Proceedings of the 41st IEEE Conference on Decision and Control 2002., 1Co-Authors: Chun-hsiung FangAbstract:Using the technique of linear Matrix inequalities (LMIs) and a parameter-dependent Matrix, new sufficient conditions for stability robustness of uncertain descriptor systems axe provided. The considered systems are very general, in which uncertainties appear in both the State Matrix and the derivative State Matrix (or the advanced State Matrix). Since the conditions axe expressed in terms of LMIs involving only the vertices of polytope domain, it is computationally simple to determine the robust stability of uncertain descriptor systems.
Bernard Budiansky - One of the best experts on this subject based on the ideXlab platform.
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Steady-State Matrix cracking of ceramics reinforced by aligned fibers and transforming particles
Journal of the Mechanics and Physics of Solids, 1993Co-Authors: Yingqing Lawrence Cui, Bernard BudianskyAbstract:Abstract M atrix fracture in ceramic composites is analysed for ceramics which are reinforced by both dilatantly transforming particles and fibers which are aligned in the direction perpendicular to the crack surfaces. Numerical results for the Matrix cracking strength are given in terms of parameters associated with the individual reinforcing mechanisms, and coupling parameters that characterize their interaction. An energy balance relation governing the steady-State Matrix cracking is presented. The results suggest that transforming particles could increase the Matrix cracking strength of aligned-fiber composites by up to about a factor of two.
Qinghua Zhang - One of the best experts on this subject based on the ideXlab platform.
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Local adaptive observer for linear time-varying systems with parameter-dependent State matrices
2018Co-Authors: Romain Postoyan, Qinghua ZhangAbstract:We present an adaptive observer for linear time-varying systems whose State Matrix depends on unknown parameters. We first assume that the State Matrix is affine in these parameters. In this case, the proposed observer generates State and parameter estimates, which exponentially converge to the plant State and the true parameter, respectively, provided a persistence of excitation condition holds and the unknown parameters lie in a neighborhood of some known nominal values. Hence, some prior knowledge on the unknown parameters is required, but not on the State. We then modify the adaptive observer and its convergence analysis to systems whose State Matrix is smooth, instead of being affine, in the unknown parameters. The convergence is approximate, and no longer exponential, in this case. An example is provided to illustrate the results, for which the required distance between the unknown parameters and their nominal values is investigated numerically.
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CDC - Local Adaptive Observer for Linear Time-Varying Systems with Parameter-Dependent State Matrices
2018 IEEE Conference on Decision and Control (CDC), 2018Co-Authors: Romain Postoyan, Qinghua ZhangAbstract:We present an adaptive observer for linear time-varying systems whose State Matrix depends on unknown parameters. We first assume that the State Matrix is affine in these parameters. In this case, the proposed observer generates State and parameter estimates, which exponentially converge to the plant State and the true parameter, respectively, provided a persistence of excitation condition holds and the unknown parameters lie in a neighborhood of some known nominal values. Hence, some prior knowledge on the unknown parameters is required, but not on the State. We then modify the adaptive observer and its convergence analysis to systems whose State Matrix is smooth, instead of being affine, in the unknown parameters. The convergence is approximate, and no longer exponential, in this case. An example is provided to illustrate the results, for which the required distance between the unknown parameters and their nominal values is investigated numerically.
Anders Rantzer - One of the best experts on this subject based on the ideXlab platform.
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Minimax Adaptive Control for State Matrix with Unknown Sign
arXiv: Optimization and Control, 2019Co-Authors: Anders RantzerAbstract:For linear time-invariant systems having a State Matrix with uncertain sign, we formulate a minimax adaptive control problem as a zero sum dynamic game. Explicit expressions for the optimal value function and the optimal control law are given in terms of a Riccati equation. The optimal control law is adaptive in the sense the past data is used to estimate the uncertain sign for prediction of future dynamics. Once the sign has been estimated, the controller behaves like standard H-infinity optimal State feedback.
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optimal h State feedback for systems with symmetric and hurwitz State Matrix
Advances in Computing and Communications, 2016Co-Authors: Carolina Lidstrom, Anders RantzerAbstract:We address H∞ State feedback and give a simple form for an optimal control law applicable to linear time invariant systems with symmetric and Hurwitz State Matrix. More specifically, the control law as well as the minimal value of the norm can be expressed in the matrices of the system's State space representation, given separate cost on State and control input. Thus, the control law is transparent, easy to synthesize and scalable. If the plant possesses a compatible sparsity pattern, it is also distributed. Examples of such sparsity patterns are included. Furthermore, if the State Matrix is diagonal and the control input Matrix is a node-link incidence Matrix, the open-loop system's property of internal positivity is preserved by the control law. Finally, we give an extension of the optimal control law that incorporate coordination among subsystems. Examples demonstrate the simplicity in synthesis and performance of the optimal control law.
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ACC - Optimal H∞ State feedback for systems with symmetric and Hurwitz State Matrix
2016 American Control Conference (ACC), 2016Co-Authors: Carolina Lidstrom, Anders RantzerAbstract:We address H∞ State feedback and give a simple form for an optimal control law applicable to linear time invariant systems with symmetric and Hurwitz State Matrix. More specifically, the control law as well as the minimal value of the norm can be expressed in the matrices of the system's State space representation, given separate cost on State and control input. Thus, the control law is transparent, easy to synthesize and scalable. If the plant possesses a compatible sparsity pattern, it is also distributed. Examples of such sparsity patterns are included. Furthermore, if the State Matrix is diagonal and the control input Matrix is a node-link incidence Matrix, the open-loop system's property of internal positivity is preserved by the control law. Finally, we give an extension of the optimal control law that incorporate coordination among subsystems. Examples demonstrate the simplicity in synthesis and performance of the optimal control law.
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Optimal Distributed H-infinity State Feedback for Systems with Symmetric and Hurwitz State Matrix
arXiv: Optimization and Control, 2015Co-Authors: Carolina Lidstrom, Anders RantzerAbstract:We address H-infinity structured static State feedback and give a simple form for an optimal control law applicable to linear time invariant systems with symmetric and Hurwitz State Matrix. More specifically, the control law as well as the minimal value of the norm can be expressed in the matrices of the system's State space representation, given separate cost on State and control input. Thus, the control law is transparent, easy to synthesize and scalable. Furthermore, if the plant possess a compatible sparsity pattern it is also distributed. Examples of such sparsity patterns are included. Furthermore, we give an extension of the optimal control law that incorporate coordination among subsystems. We demonstrate by a numerical example that the derived optimal controller is equal in performance to an optimal controller derived by the riccati equation approach.
