Fault Detection, Classification and Location in Power Transmission Line System using Artificial Neural Networks

Abstract

Transmission lines, among other electrical power system components, suffer from unexpected failures due to various random causes. These failures interrupt the reliability of the operation of the power system. When unpredicted faults occur protective systems are required to prevent the propagation of these faults and safeguard the system against the abnormal operation resulting from them. The functions of these protective systems are to detect and classify faults as well as to determine the location of the faulty line when a fault is detected in the voltage and/or current line magnitudes. Once the fault is detected and classified the protective relay sends a trip signal to a circuit breaker(s) in order to disconnect (isolate) the faulted line. The features of neural networks, such as their ability to learn, generalize and parallel processing, among others, have made their applications on many systems ideal. The use of neural networks as pattern classifiers is among their most common and powerful applications. This thesis presents a back-propagation artificial neural network architecture approach to detection, classification and isolation (location) of faults in transmission line systems. The objective is to implement a complete scheme for distance protection of a transmission line system. In order to perform this goal, the distance protection task is subdivided into different neural networks for fault detection, fault identification (classification) as well as fault location in different zones.