An Artificial intelligence approach to detection of high impedance fault

Abstract: This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a back propagation neural network as a classifier for identifying high impedance arc- type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned back propagation network gives quicker response.

Keywords: Fault identification, distribution networks, high-impedance arc-faults, feature vector, back-propagation network.