FAULT IDENTIFICATION SYSTEM FOR ELECTRIC POWER TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS

Abstract

Electric power transmission line faults hinder the continuity of electric power supplied and increase the system downtime thereby increasing the loss of electric power transmitted. Early fault detection and classification leads to prompt clearance of faults with an attendant effect of improved reliability and efficiency of the power system network. In view of this, this paper develops an arti-ficial neural network (ANN)-based detector and classifier to indicate and classify respectively a fault on Nigeria 33-kV electric power transmission lines. The transmission lines are modeled in Simulink using SimPowerSystems toolbox in MATLAB. Fault simulations are carried out, and the resulting instantaneous values of voltages and currents are used to develop the proposed fault identification sys-tem using multilayer perceptron feedforward artificial neural networks with backpropagation algorithm. Results are presented to vali-date the effectiveness and efficiency of the developed identification system for detecting and classifying faults. The Mean Square Error (MSE), linear regression and the confusion matrix are used as performance evaluators for the system. The ANN-based identification system achieved MSE of 4.77399e-10 and an accuracy of 100% for fault detection. This indicates that the performance of the developed ANN-based identification system is highly satisfactory and may be practically implemented on the Nigeria transmission lines.