Distributed Power Parameter Measurement Method Using a Strong Tracking Factor Kalman Filter Based on Particle Swarm Optimization

Distributed power sources can reduce transmission and network losses, improve grid stability and reliability, and lower system operation costs, which are of great significance for improving the economy and environmental protection of the power system. The signals in Distributed power systems often c...

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Bibliographic Details
Published in2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems (ICPICS) pp. 472 - 476
Main Authors Yin, Xiang, Fan, Chuanguang, Yang, Xinyuan, Fu, Gaoshan, Zang, Siqi, Li, Zhangcheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.07.2023
Subjects
Atmospheric measurements
Control systems
distributed power
electrical signal
measurement
Particle measurements
particle swarm
Power measurement
Power system stability
Real-time systems
strong tracking Kalman filter
Target tracking
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Summary:Distributed power sources can reduce transmission and network losses, improve grid stability and reliability, and lower system operation costs, which are of great significance for improving the economy and environmental protection of the power system. The signals in Distributed power systems often contain various noise, harmonic, and inter-harmonic interferences, which seriously affect the accuracy of phasor measurement. To address this issue, a particle swarm optimization (PSO) algorithm is used to optimize the strong tracking factor based on the strong tracking Kalman filter (STKF), and measure the amplitude, frequency, and phase angle of power signals. The results show that compared with the strong tracking Kalman filter, the PSO-STKF algorithm improves the accuracy and convergence speed of phasor measurement. The research enriches the content of signal measurement in power systems.
ISSN:2834-8567
DOI:10.1109/ICPICS58376.2023.10235737