Support Vector Machines for classification and locating faults on transmission lines |
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| Authors: | Sami Ekici |
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| Affiliation: | 1. Center of Engineering, Modelling and Social Science, Federal University of ABC, Av. Dos Estados, 5001, CEP 09210-580, Santo André, SP, Brazil;2. School of Engineering of São Carlos, University of São Paulo, Av. Trabalhador São-carlense, 400, CEP 13566-590, São Carlos, SP, Brazil;1. Siksha O Anusandhan University, Bhubaneswar 751030, India;2. Multimedia University, Cyberjaya, Malaysia;1. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi 110025, India;2. Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India |
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| Abstract: | This paper presents a new approach to classify fault types and predict the fault location in the high-voltage power transmission lines, by using Support Vector Machines (SVM) and Wavelet Transform (WT) of the measured one-terminal voltage and current transient signals. Wavelet entropy criterion is applied to wavelet detail coefficients to reduce the size of feature vector before classification and prediction stages. The experiments performed for different kinds of faults occurred on the transmission line have proved very good accuracy of the proposed fault location algorithm. The fault classification error is below 1% for all tested fault conditions. The average error of fault location in a 380 kV–360-km transmission line is below 0.26% and the maximum error did not exceed 0.95 km. |
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