Artificial Intelligence Based Fault Location in a Distribution System

Abstract

A hybrid technique for fault distance estimation in a distribution line with wind farm is presented in this paper. Here, one cycle of post fault current samples are taken for fault location from the distributed generation end. The collected samples are then decomposed by wavelet transform and thereafter six statistical features are extracted from the reconstructed detail coefficients of the current signal. Further best features are selected from the total feature set by forward feature selection method. These selected features are then fed as input to the artificial neural network for fault location. In the proposed method, the simulation conditions for the test pattern are completely different from the train one in order to make it robust. Simulation result shows that the proposed hybrid fault location method gives high accuracy for the distribution system.