Detection of Power Quality Disturbance of the Best ASD Based on Dynamic PSO Search

This paper mainly studies the power quality disturbance feature detection and optimization method based on atomic sparse decomposition algorithm. In order to solve the problem of large amount of matching pursuit algorithm, this paper proposes a Particle Swarm Optimization dynamic search (PSO-DS) algorithm based on the PSO optimization algorithm, using the prior information provided by the fast Fourier transform and wavelet transform to search for parameters. The range and optimization of the search for the best atom are optimized. The simulation of the example shows that the PSO-DS algorithm can effectively extract the signal features with less decomposition times, avoiding the generation of unrelated and erroneous components, improving the detection accuracy of the disturbance signal and the simplicity and accuracy of the signal representation. (paper)