Support vector machine based fault classification and location of a long transmission line

Abstract

This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400kV, 300km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear) and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21%) and least fault distance estimation error (<0.21%) for all discussed cases. In order to verify the accuracy of the proposed method, a comparison is carried out with methods published by other researchers. Separate investigation is also carried out with the transmission line placing thyristor controlled series capacitor in the middle and applying the same proposed method. It is observed from the test results of the thyristor controlled series capacitor based transmission line model that fault classification gives a high accuracy of 98.36% and absolute fault location error is >0.29%.