Support Vector Machines for classification and locating faults on transmission lines

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• 作者：Sami Ekici ; ^{sekici@firat.edu.tr} ; ^{sami.ekici@gmail.com}
• 关键词：Support Vector Machines ; Wavelet Transform ; Transmission line faults
• 刊名：Applied Soft Computing Journal
• 出版年：2012
• 期刊代码：9_15684946
• 类别：cp
• 出版时间：June, 2012
• 卷：12
• 期：6
• 页码：1650-1658
• 文件大小：506 K

摘要

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.