首页|Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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This paper investigates the anomaly-resistant decen-tralized state estimation(SE)problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines.Two classes of mea-surements(i.e.,local measurements and edge measurements)are obtained,respectively,from the individual area and the transmis-sion lines.A decentralized state estimator,whose performance is resistant against measurement with anomalies,is designed based on the minimum error entropy with fiducial points(MEEF)cri-terion.Specifically,1)An augmented model,which incorporates the local prediction and local measurement,is developed by resorting to the unscented transformation approach and the sta-tistical linearization approach;2)Using the augmented model,an MEEF-based cost function is designed that reflects the local pre-diction errors of the state and the measurement;and 3)The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information.Finally,simulation experi-ments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Decentralized state estimation(SE)measurements with anomaliesminimum error entropyunscented Kalman filterwide-area power systems

Bogang Qu、Zidong Wang、Bo Shen、Hongli Dong、Hongjian Liu

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College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China

Department of Computer Science,Brunel University London,Uxbridge,Middlesex UB8 3PH,UK

College of Information Science and Technology,Donghua University,Shanghai 200051,and also with the Engineering Research Center of Digitalized Textile and Fashion Technology,Ministry of Education,Shanghai 201620,China

Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control,Northeast Petroleum University,Daqing 163318,and the Sanya Offshore Oil &Gas Research Institute,Northeast Petroleum University,Sanya 572024,China

Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment,Ministry of Education,Anhui Polytechnic University,Wuhu 241000,and also with the School of Mathematics and Physics,Anhui Polytechnic University,Wuhu 241000,China

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国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金Program of Shanghai AcademicTechnology Research Leader of ChinaHainan Province Science and Technology Special Fund of China安徽省自然科学基金Alexander von Humboldt Foundation of Germany

61933007U21A201962273005622730886230330120XD1420100ZDYF2022SHFZ1052108085MA07

2024

10.1109/JAS.2023.123795

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(1)
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