Power quality disturbance detection method based on optimized kernel extreme learning machine

Abstract

In order to improve the accuracy of rapid detection of power quality, a power quality disturbance (PQD) classification method based on kernel-based extreme learning machine (KELM) is proposed, and chaos optimization is used to improve the global optimization performance of the particle swarm algorithm. This method first uses KELM to establish a classification model, and then uses an improved chaotic particle swarm optimization (CPSO) to optimize the parameters of KELM. Comparative analysis of example simulation results shows that the algorithm has higher classification accuracy and improves the reliability of power quality disturbance detection.