Fault Classification for DG integrated Hybrid Power System using Wavelet Neural Network Approach

This paper presents a novel fault classification technique which uses Wavelet Neural Network (WNN) based approach. The data for the fault classification is obtained using MATLAB Simulation program for 30kv, 100km, Distributed generators (DG) integrated hybrid network. The two DGs connected in the proposed test system are Wind DG and Photovoltaic (PV) DG. The target of this work is to classify the fault correctly in the proposed test system. The data set collected from the point of common coupling (PCC) is with various conditions of fault with a distinct resistant level. It is clear from the results that the proposed method of classification of faults using WNN is able to correctly recognize the faults with very high accuracy in the simulated model of hybrid network.