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 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.