Support Vector Machines for classification and locating faults on transmission lines

Authors:
Sami Ekici
Affiliations:
Firat University, Technology Faculty, Energy Systems Engineering, 23119, Elazig, Turkey
Venue:
Applied Soft Computing
Year:
2012

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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 380kV-360-km transmission line is below 0.26% and the maximum error did not exceed 0.95km.