Resilient dynamic state estimation for multi-machine power system with partial missing measurements

Wang, Yi, Wang, Yaoqiang, Sun, Yonghui, Dinavahi, Venkata, Liang, Jun ORCID: https://orcid.org/0000-0001-7511-449X and Wang, Kewen 2024. Resilient dynamic state estimation for multi-machine power system with partial missing measurements. IEEE Transactions on Power Systems 39 (2) , pp. 3299-3309. 10.1109/TPWRS.2023.3287151

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Abstract

Accurate tracking the dynamics of power system plays a significant role in its reliability, resilience and security. To achieve the reliable and precise estimation results, many advanced estimation methods have been developed. However, most of them are aiming at filtering the measurement noise, while the adverse affect of partial measurement missing is rarely taken into account. To deal with this issue, a discrete distribution in the interval [0,1] is introduced to depict mechanism of partial measurement data loss that caused by the sensor failure. Then, a resilient fault tolerant extended Kalman filter (FTEKF) is designed in the recursive filter framework. Eventually, extensive simulations are carried on the different scale test systems. Numerical experimental results illustrate that the resilience and robustness of the proposed fault tolerant EKF method against partial measurement data loss.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0885-8950
Date of First Compliant Deposit:	13 July 2023
Last Modified:	17 Apr 2024 17:16
URI:	https://orca.cardiff.ac.uk/id/eprint/160911

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