Abstract
Research on power transmission lines has been the subject of several studies aiming to provide relevant information to electrical system users. One of the focuses of this study area is transmission line fault classification. This paper presents an approach for fault classification using higher-order statistics and an artificial neural network-based classifier. A detector based on Euclidean distance was implemented to reduce classifier complexity. The proposed method takes advantage of requiring only 1⁄32 cycles of postfault data to perform the classification; therefore, it is suitable for real-time processing. The proposed method classified 10 classes of faults with global efficiency above 97%.
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