Classification and location of single line to ground faults in double circuit transmission lines using artificial neural networks

Abstract

Fault detection, classification and location from the relay end in a double circuit transmission line are challenging tasks because of mutual coupling between the two circuits. In this paper, combined unsupervised and supervised neural networks-based fault detection, classification and distance location techniques are presented for a double circuit line. This technique does not require communication link to retrieve the remote end data. Zero sequence current compensation for healthy phases can also be avoided. Artificial neural network employs a reduced set of input features, i.e., the fundamental components of three phase voltages and the six phase currents of the two parallel lines at one end of the line only. The simulation results show that single phase-to-ground faults can be correctly classified and located after one cycle from the inception of fault. The complexity of the possible types of faults, fault locations, high path fault resistances, fault inception angles, mutual coupling effects and remote end in-feed do not affect the performance.