High speed transmission system directional protection using an Elman network

Detection of the direction of a fault on a transmission line is essential to the proper performance of a power system. It would be desirable to develop a high speed and accurate approach to determine the fault direction far different pourer system conditions. To classify forward and backward faults on a given line, neural network's abilities in pattern recognition and classification could be considered as a solution. To demonstrate the applicability of this solution, neural network technique is employed and a novel Elman recurrent net work is designed and trained. Details of the design procedure and the results of performance studies with the proposed network are given and analysed in the paper. System simulation studies show that the proposed approach is able to detect the direction of a fault on a transmission line rapidly and correctly. It is suitable to realize a very fast transmission line directional comparison protection scheme.