American Journal of Engineering Research (AJER) 2013 www.ajer.org Page 69 American Journal of Engineering Research (AJER) e-ISSN : 2320-0847 p-ISSN : 2320-0936 Volume-02, Issue-06, pp-69-75 www.ajer.org Research Paper Open Access Faults Detection in Power Systems Using Artificial Neural Network Eisa Bashier M Tayeb School of Electrical and Nuclear Engineering, College of Engineering Sudan University of Science &Technology; Khartoum – SUDAN Abstract:- Electrical power systems suffer from unexpected failures due to various random causes. Unpredicted faults that occur in power systems are required to prevent from propagation to other area in the protective system. The functions of the protective systems are to detect, then classify and finally determine the location of the faulty line of voltage and/or current line magnitudes. Then at last, for isolation of the faulty line the protective relay have to send a signal to the circuit breaker. The ability to learn, generalize and parallel processing, pattern classifiers is powerful applications of NN used as an intelligent means for detection. This paper presents neural network NN architecture for fault detection in a transmission line power system. It aims to implement complete scheme for distance protection that subdivided into different neural networks zones. Single phase to ground, double phase and double phase to ground faults are considered. As a result a protection relaying system for the power transmission line systems can be done using the NNBP architecture. Keywords: - Power system protection, fault identification, neural network architecture, Transmission lines protection. I. INTRODUCTION The electrical system faults are the greatest threat to the continuity of electricity supply. Faults on electric power systems are an unavoidable problem. Hence, a well-coordinated protection system must be provided to detect and isolate faults rapidly so that the damage and disruption caused to the power system is minimized. The clearing of faults is usually accomplished by devices that can sense the fault and quickly react and disconnect the faulty section. It is therefore an everyday fact of life that different types of faults occur on electrical systems, however infrequently, and at random locations. Faults can be broadly classified into two main areas which have been designated as active and passive [1]. Electrical power systems control centers contain a large number of alarms received as a result of different types of faults. To protect these systems, the faults must be detected and isolated accurately. Majority of short-circuit faults tend to occur on overhead lines [2]. The operators in the control centers have to deal with a large amount of data to get the required information about the faults. Through the years artificial neural networks [3, 4], have been invented with both biological ideas and control applications in mind, and the theories of the brain and nervous system have used ideas from control system theory [5]. The neural network represents a network with a finite number of layers consisting of solitary elements that are similar to neurons with different types of connection between layers. The number of neurons in the layers is selected to be sufficient for the provision of the required problem solving quality. The number of layers is desired to be minimal in order to decrease the problem solving time [1, 6]. Basically, we can design and train the neural networks for solving particular problems which are difficult to solve by the human beings or the conventional computational algorithms. The computational meaning of the training comes down to the adjustments of certain weights which are the key elements of the ANN. This is one of the key differences of the neural network approach to problem solving than conventional computational algorithms. This adjustment of the weights takes place when the neural network is presented with