Transmission Line Fault Classification and Fault Zone Identification Using Artificial Neural Network

Transmission lines, among the opposite power system elements, suffer from sudden failures owing to varied random causes. These failures interrupt the dependability of the operation of the facility system. Once unheralded faults occur protecting systems square measure needed to stop the propagation of those faults and safeguard the system against the abnormal operation ensuing from them. The functions of those protecting systems square measure to observe and classify faults further on confirm the placement of the faulty line as within the voltage and/or current line magnitudes. Then once the protecting relay sends a trip signal to a circuit breaker(s) so as to disconnect (isolate) the faulty line. The options of neural networks, like their ability to be learn, generalize and multiprocessing, among others, have created their applications for several systems ideal. The utilization of neural networks as pattern classifiers is among their commonest and powerful applications. This methodology presents the utilization of back-propagation (BP) neural network specification as another method for fault classification and fault zone identification during a transmission line system. The most goal is that the implementation of complete theme for distance protection of a line system. So as to perform this, the line protection task is divided into totally different neural networks for fault classification additionally as fault location in numerous zones. Four unsymmetrical faults were discussed; single line to ground faults (LG), double line faults (LL) and double line to ground faults (LLG) and additionally two symmetrical fault cases additionally mentioned.