A Comparison of Back propagation and PSO for training RBF Neural Network for Wavelet based Detection and Classification of Power Quality Disturbances

AUTHOR(S):

P. Kanirajan, M. Joly, T. Eswaran

TITLE

A Comparison of Back propagation and PSO for training RBF Neural Network for Wavelet based Detection and Classification of Power Quality Disturbances

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ABSTRACT

This paper introduces a novel 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 commonly used for training, but it suffers from extensive computation and also convergence speed is relatively slow. Feature extracted through the wavelet is used for training. After training, the weight obtained is used to classify the power quality problems. For classification, 8 types of disturbance are taken in to account. The classification performance of RBFNN trained PSO algorithm is compared with BP algorithm. The simulation result using PSO possess significant improvement over BP methods in signal detection and classification

KEYWORDS

Power Quality, Radial basis function neural network, wavelet transformation, Back Propagation, Particle Swarm Optimization

Cite this paper

P. Kanirajan, M. Joly, T. Eswaran. (2021) A Comparison of Back propagation and PSO for training RBF Neural Network for Wavelet based Detection and Classification of Power Quality Disturbances. International Journal of Signal Processing, 6, 33-38