Support Vector Machines for classification and locating faults on transmission lines

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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► We present a Support Vector Machine based method to classify fault types and estimate fault locations on a high voltage transmission line.
► We use Discrete Wavelet Transform for analyzing the voltage and current waveforms.
► Entropy criterion is used to decrease the size of feature matrix.
► We obtain 99% accuracy by using this SVM and Wavelet based method.