An Intelligent Short-Circuit Fault Classification Scheme for Power Transmission Line

This paper focuses on presenting a novel fault detection and classification (FDC) scheme that can classify shut faults occur in overhead transmission lines with a very promising performance. Immediate diagnosis of TL faults is important to avoid power system damage. Several fault detection and classification (FDC) systems have been developed to provide a reliable solution to this problem but huge computation time, noisy data, fault inception-angle and fault resistance variation effect made those systems less acceptable. In this paper, we focus mostly on increasing the performance of the scheme while overcoming the above mentioned challenges providing more focus on the reduction of computational complexity. Consequently, an optimization algorithm is integrated with the FDC system that makes analyzing training data less complex thus saving huge amount of time. Furthermore, wavelet aided signal processing technique is used to extract only useful information from the signal eliminating noise profiles. The reliability of the proposed scheme is verified by performing the FDC operation on a real-time power system simulation.