Resilient dynamic state estimation for power system using Cauchy-kernel-based maximum correntropy cubature Kalman filter

Wang, Yi, Yang, Zhiwei, Wang, Yaoqiang, Li, Zhongwen, Dinavahi, Venkata and Liang, Jun ORCID: https://orcid.org/0000-0001-7511-449X 2023. Resilient dynamic state estimation for power system using Cauchy-kernel-based maximum correntropy cubature Kalman filter. IEEE Transactions on Instrumentation and Measurement 72 , 9002011. 10.1109/TIM.2023.3268445

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Abstract

Accurate estimation of dynamic states is the key to monitoring power system operating conditions and controlling transient stability. The inevitable non-Gaussian noise and randomly occurring denial-of-service (DoS) attacks may, however, deteriorate the performance of standard filters seriously. To deal with these issues, a novel resilient cubature Kalman filter based on the Cauchy kernel maximum correntropy (CKMC) optimal criterion approach (termed CKMC-CKF) is developed, in which the Cauchy kernel function is used to describe the distance between vectors. Specifically, the errors of state and measurement in the cost function are unified by a statistical linearization technique, and the optimal estimated state is acquired by the fixed-point iteration method. Because of the salient thick-tailed feature and the insensitivity to the kernel bandwidth (KB) of Cauchy kernel function, the proposed CKMC-CKF can effectively mitigate the adverse effect of non-Gaussian noise and DoS attacks with better numerical stability. Finally, the efficacy of the proposed method is demonstrated on the standard IEEE 39-bus system under various abnormal conditions. Compared with standard cubature Kalman filter (CKF) and maximum correntropy criterion CKF (MCC-CKF), the proposed algorithm reveals better estimation accuracy and stronger resilience.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0018-9456
Date of First Compliant Deposit:	28 June 2023
Date of Acceptance:	31 March 2023
Last Modified:	22 Nov 2024 00:30
URI:	https://orca.cardiff.ac.uk/id/eprint/160043

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