Synchronous phasor phase correction method based on particle swarm optimization and extend Kalman filter frequency tracking

Guyu Xu; Yansong Li; Jun Liu

doi:10.1117/12.2689329

10 August 2023 Synchronous phasor phase correction method based on particle swarm optimization and extend Kalman filter frequency tracking

Guyu Xu, Yansong Li, Jun Liu

Author Affiliations +

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127482C (2023) https://doi.org/10.1117/12.2689329
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

When the frequency of power signal is offset, it is difficult to achieve synchronous sampling by using discrete Fourier transform in synchronous phasor measurement. The fence effect caused by this will seriously affect the precision of synchronous phasor measurement, especially the precision of phase measurement. Therefore, this paper proposes an improved DFT synchronous phasor measurement algorithm. Firstly, an algorithm for frequency tracking, which is based upon the extended Kalman filter, is put forward. Besides, the algorithm for particle swarm optimization is described with a view to optimizing the noise weight of Kalman gain. Based on the frequency offset rate obtained by frequency tracking, the improved DFT algorithm is adopted for the sake of attaining the corrected phase result. These consequences of simulation indicate that the frequency tracking error of this algorithm doesn`t reach 0.015Hz, and the phase calculation error doesn`t reach 0.018°, which satisfies the accuracy engineering demands, and the convergence speed is fast.

Citation Download Citation

Guyu Xu, Yansong Li, and Jun Liu "Synchronous phasor phase correction method based on particle swarm optimization and extend Kalman filter frequency tracking", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127482C (10 August 2023); https://doi.org/10.1117/12.2689329

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KEYWORDS

Detection and tracking algorithms

Particle swarm optimization

Signal filtering

Error analysis

Error control coding

Fourier transforms

Xenon

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