Detection and classification of faults in transmission lines using the maximum wavelet singular value and Euclidean norm
作者： Daniel Guillen , Gina Idarraga , Juan Manuel Ramirez , Alejandro Zamora , Mario Roberto Arrieta Paternina
DOI: 10.1049/IET-GTD.2014.1064
关键词:
摘要: In this study, a novel algorithm for detecting and classifying faults in high-voltage transmission lines is proposed. The based on the discrete wavelet transform (DWT) singular value decomposition (SVD). DWT used extracting currents’ high-frequency components under fault conditions. Signals each condition are scaled frequency, order to build matrix. By means of SVD, maximum calculated employed proposal. attained results exhibit that represents good indicator issue. This approach power systems called value. Phase-to-ground, two-phase ground, three-phase faults’ simulations different impedances carried out by DIgSILENT Power Factory. analysed conditions evaluated demonstrating proposal reduces computational burden time detection.
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