Estimation of the fault location and the voltage error in measurement at the relay point using radial basis ANN

Recently, in digital distance relaying, different methods have been proposed for fault location estimation using one-end data and two-end data. The first method is more practical and economical since no communication channels and special devices are needed as in method "2". The first part of this paper proposes a new accurate approach for fault section estimation using probabilistic neural network based on the phase voltages and line currents phasor measurements from one end of the power transmission line. This approach takes the combined effect of fault resistance and load flow into consideration in the training data and assumes fault type is available. The second part of the paper suggests a recent method to estimate the error in voltage measurements at the relAy point due to the existence of ground resistance and load current using generalized regression neural network (GRNN). Finally, modular SCGNN operates successively with PNN and GRNN to estimate the distance to the fault accurately at each section with the aid of positive, negative, zero sequence components of voltages and zero sequence current. The data required for the training of ANN is measured and processed using MATLAB Simulink.