Distribution network fault section identification and fault location using artificial neural network

Abstract

In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics are obtained by wavelet transform on three-phase currents and sequences and extracting the high frequency characteristic. Since high frequencies are generated during the occurrence of the fault, signal information could be extracted using wavelet transform. After wavelet transform, the entropies of the minor components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters and their combinations are very important. Finally, one could estimate the fault section, fault location, and fault resistance after implementing the neural network. The simulation results show the good performance of neural network for the faults in different angles, locations, and resistances.