Fault Classification Scheme Based on the Adaptive Resonance Theory Neural Network for Protection of Transmission Lines

Abstract

This article presents a new approach to classify various types of power system faults using wavelet transforms and the adaptive resonance theory. The key idea underlying the approach is to decompose a given disturbance signal into other signals, which represents a smoothed and detailed version of the original signal. The proposed technique consists of a preprocessing unit based on discrete wavelet transform in combination with adaptive resonance theory. Through wavelet analysis, faults are decomposed into a series of wavelet components, each of which is a timedomain signal that covers a specific octave frequency band. The parameters selected for fault classification are the detailed coefficients of all phase current signals that are collected only at the sending end of a transmission line. The information is then fed into adaptive resonance theory for classifying the faults. The study is performed on a sample power system network. Extensive simulation studies carried out using MATLAB (http://www.mathworks.com) shows that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions, but it is also reliable, fast, and computationally efficient tool.

A fault classification technique for transmission lines using an improved alienation coefficients technique

“…In , three different ANNs are used to classify a fault and identify its location. An application of the adaptive resonance theory for fault classification of transmission lines using wavelets is presented in .…”