Detection and Classification of Faults in Power Transmission Lines Using Functional Analysis and Computational Intelligence

The transmission line is the most vulnerable element of any electrical power system due to its large physical dimension. As a consequence, many fault diagnosis algorithms have been proposed in the literature. In general, most proposals use signal-processing analysis and computational intelligence. In this paper, a new model to functionally represent the phases of a transmission line is proposed. The detection and classification strategy are developed from the analysis of the model's parameters and were evaluated using a set of simulated faults and a real database. The results show that the proposed model detects faults very quickly, using a vastly simplified mathematical process, and is able to classify faults accurately.