Recursive Strong Tracking Filtering for Power Harmonic Detection With Outliers-Resistant Event-Triggered Mecha-nism

Abstract

Article Recursive Strong Tracking Filtering for Power Harmonic Detection With Outliers-Resistant Event-Triggered Mechanism Xingzhen Bai 1,*, Guhui Li 1, Mingyu Ding 2, Liqun Yu 1, and Yufeng Sun 1 1 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Shandong 266590, China 2 State Grid Dong’e County Power Supply Company in Shandong Province, Dong'e, 252200, China * Correspondence: xzbai@sdust.edu.cn Received: 24 September 2023 Accepted: 25 December 2023 Published: 24 December 2024 Abstract: This paper is concerned with the problem of power harmonic detection subject to communication resource constraints and measurement outliers. A dynamic tracking model is established to capture the dynamics of harmonic signals considering that the underlying system is subject to multiplicative noises, additive noises and outliers. Furthermore, an outlier-resistant event-triggered mechanism is designed to prevent the transmission of unnecessary measurements and outliers. In order to guarantee the satisfactory filtering performance, this paper aims to design a recursive strong tracking filtering algorithm under the event-triggered mechanism, where an upper bound on the filtering error covariance matrix is obtained by solving a set of Riccati difference equations, and minimized to recursively compute the filter gain matrix. Finally, the effectiveness of the proposed algorithm is verified through carrying out two sets of simulations.