Faults detection, location, and classification of the elements in the power system using intelligent algorithm

Abstract

This study proposes an intelligent protection relay design that uses artificial neural networks to secure electrical parts in power infrastructure from different faults. Electrical transformer and transmission lines are protected using intelligent differential and distance relay, respectively. Faults are categorized, and their locations are pinpointed using three-phase current values and zero-current characteristics to differentiate between non-earth and ground faults. The optimal aspects of the artificial neural network were chosen for optimal results with the least possible error. Levenberg-Marquardt was established as the ideal training technique for the suggested system comprising the differential relay. Levenberg-Marquardt was the optimal training technique for the proposed framework consisting of the differential relay. Fault detection and categorization were performed using 20 and 50 hidden layers, and the corresponding error rates were 9.9873e-3 and 1.1953e-29. In the context of fault detection by the distance relay, the hidden layer neuron counts were 400, 250, and 300 for fault detection, categorization, and location; training error rates were 7.8761e-2, 1.2063e-6, and 1.1616e-26, respectively.