Application of RBF neural network to fault classification and location in transmission lines

Abstract

The application of radial basis function (RBF) neural networks for fault classification and location in transmission lines is presented. Instantaneous current/voltage samples have been used as inputs to artificial neural networks (ANNs). Whereas, for fault classification, prefault and postfault samples of only the three-phase currents are sufficient, for fault location, postfault samples of both currents and voltages of the three phases are necessary. To validate the proposed approach simulation studies have been carried out on two simulated power-system models: one in which the transmission line is fed from one end and another, in which the transmission line is fed from both ends. The models are subjected to different types of faults at different operating conditions for variations in fault location, fault inception angle and fault point resistance. The results of the simulation studies which are presented confirm the feasibility of the proposed approach.