A fast and accurate distance relaying scheme using an efficient radial basis function neural network

Abstract

The paper presents a new approach for classification and location of faults on a transmission line using a newer version of radial basis function neural network (RBFNN) which provides a more efficient approach for training and computation. The input data to the RBFNN comprise the normalised peak values of the fundamental power system voltage and current waveforms at the relaying location obtained during fault conditions. The extraction of the peak components is carried out using an extended Kalman filter (EKF) suitably modelled to include decaying d.c., third and fifth harmonics along with the fundamental. The fault training patterns required using the efficient version of RBF neural network are much less in comparison to the conventional RBF network and the choice of neurons and the parameters of the network are systematically arrived without resorting to trial and error calculations. The new approach provides a robust classification of different fault types for a variety of power system operating conditions with resistance in the fault path. Further a new fault location strategy is formulated using four neural networks, one each for the major category of faults like LG, LL, LLG and LLL faults. The proper feature selection for the networks results in an accurate and fast distance relaying scheme.