RBFNN Based Power Quality Issues Detection and Classification using Wavelet-PSO

Abstract

This paper introduces a new approach to detect and classify power quality disturbances in the power system using Radial Basis Function Neural Networks (RBFNN) trained by Particle Swarm Optimization (PSO).Back Propagation (BP) algorithm is the most frequently used for training, but it suffers from extensive computation and also convergence speed is relatively slow. Feature mined through the wavelet is used for training. After training, the weight obtained is used to classify the power quality issues. For classification, 8 types of disturbance are taken in to explanation. The classification performance of RBFNN trained PSO algorithm is matched with BP algorithm. The simulation result using PSO have significant improvement over BP methods in signal detection and classification.