Fault classification in power systems using artificial neural networks

Recent applications of neural networks to power system fault diagnosis have provided positive results and shown advantages in process speed over conventional approaches. This paper describes the application of a Kohonen neural network and the backpropagation network to fault detection and classification using the fundamental components of currents and voltages. The Kohonen network is selected for its excellent pattern classification capability while the backpropagation method is chosen for comparison since it is the most commonly used ANN scheme. The Electromagnetic Transients Program is used to obtain fault patterns for the training and testing of neural networks. Accurate classifications are obtained for all types of possible short circuit faults on test systems representing high voltage transmission lines. Shorter training time makes the Kohonen network more suitable for power system fault diagnosis. The method introduced in the paper can be easily extended to any size power system since the only information required for the NN to function are those that are recorded at substation fault recorders. With fast NN hardware now becoming available, on-line implementation is only a question of economics.