Fault Detection and Classification of a Transmission Line Using Discrete Wavelet Transform & Artificial Neural Network

Abstract

This paper gives an analyzing study on fault detection and classification in a long transmission line which is series compensated using artificial neural network and wavelet transform. The proposed scheme makes use of one cycle pre fault and one cycle post fault samples of the three phase current signals to find the ground current signal. Daubechies is used as the mother wavelet while using the discrete wavelet transform technique.The differential energy, based on discrete wavelet transform is applied to feed a system, designed for the classification of all eleven fault types. Finally, the optimal features which are the energies obtained from discrete wavelet transform of the current signals selected are fed to neural networks for the purpose of fault classification. The reliability of the suggested technique is experienced in a 735-kV, 50 Hz power systems under altered operating settings using MATLAB . The fault was detected and classified using discrete wavelet transform as well as artificial neural network. The results indicate that the proposed scheme can correctly classify every possible fault with large variations in system conditions.