Artificial neural network approach to fault classification for double circuit transmission lines

A novel application of neural network approach to protection of double circuit transmission line is demonstrated in this paper. Different system faults on a protected transmission line should be detected and classified rapidly and correctly. The proposed method uses current signals to learn the hidden relationship in the input patterns. Using the proposed approach, fault detection, classification and faulted phase selection could be achieved within a quarter of cycle. An improved performance is experienced once the neural network is trained sufficiently and suitably, thus performing correctly when faced with different system parameters and conditions. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.