High Impedance Fault Classification Using Wavelet Transform and Artificial Neural Network

This paper presents a new technique based on the combination of wavelet transform(WT) and Artificial neural networks (ANNs) for addressing the problem of high impedance faults (HIFs) detection. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle and artificial neural networks. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and extracts special features to train ANNs. Classification of High Impedance faults have been done with six neural networks namely Back propagation network, Cascade correlation network, Radial Basis Function, Learning vector quantization, NARX network, AdaBoost classifier. Comparison of all these methods are shown. The signal data of several HIFs, low impedance faults (LIFs), Transients and normal switching events have been obtained by the simulation of a real distribution network under these different operations conditions, using SimPowerSystem Block set of MATLAB.