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

摘要

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.