A Wavelet-Neural Network-Based Technique for Fault Diagnostics in Power System

This paper presents a hybrid technique using a signal processing method and an intelligent scheme for power system fault diagnostics. The protection of a power line is critical for maintaining a sustainable power supply. Moreover, electrical protection schemes are required to distinguishes faults accordingly. In the present work, the discrete wavelet transform method is utilized to break down the fault current signal into sub-signal bands. Furthermore, the neural network (NN) algorithm scheme is applied to diagnose different faults which may occur in power grid network. The performance of the protection scheme relies mostly on the ability of the scheme to accurately classify fault. In the present work, the particle swarm optimization (PSO) method is implemented to evaluate the input parameters of the NN classifier. The presented results show that the ANN classified the faults with an accuracy margin of 99%.