A Comparison Study of Learning Algorithms for Estimating Fault Location

Abstract

Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current signal acted as traveling wave fault location technique. Six types commonly used backpropagation training algorithm were selected including the Levenberg-Marquardt, Bayesian Regulation, Conjugate gradient backpropagation with Powell-Beale restarts, BFGS quasi-Newton, Conjugate gradient backpropagation with Polak-Ribiere updates and Conjugate gradient backpropagation with Fletcher-Reeves updates. The proposed fault location method is tested with varying fault location, fault types, fault resistance and inception angle. The performance of each training algorithm is evaluated by goodness-of-fit (R²), mean square error (MSE) and Percentage prediction error (PPE). Simulation results show that the best of training algorithm for estimating fault location is Bayesian Regulation (R² = 1.0, MSE = 0.034557 and PPE = 0.014%).