Artificial neural-network-based fault location for power distribution lines using the frequency spectra of fault data

Abstract

This study presents an artificial neural-network (ANN)-based digital fault classification and location algorithm for medium voltage (MV) overhead power distribution lines with load taps and embedded remote-end source. The algorithm utilizes frequency spectra of voltage and current samples which are recorded by the digital relay at the substation. In the algorithm, to extract useful information for ANN inputs, the frequency spectral analysis of voltage and current waveforms has been carried out using Fast Fourier Transform. To classify and locate the shunt faults on an MV distribution system, a multilayer perceptron neural network (MLPNN) with the standard back-propagation technique has been used. A 34.5 kV overhead distribution system has been simulated using MATLAB/Simulink, and the results are used to train and test the ANNs. The technique takes into account all the practical aspects of real distribution system, such as errors, originated from instrument transformers and interface. The ANN-based fault location technique has been extensively tested for a realistic model and gives satisfactory results for radial overhead distribution systems with load taps and in the presence of remote-end source connection.