Support vector machine for fast fault detection and classification in modern power systems using quarter cycle data

Abstract

This work proposes a support vector machine (SVM) based fault detection and classification method for a transmission system. The novelty of this work is that the time and frequency series parameters at both the ends of a line, amounting to 144, computed over the most recent eight samples, are considered the input data for the developed SVM model. With this higher number of features for the SVM model, the power system faults are detected and classified with a quarter cycle data of power frequency. The developed SVM, trained with operating scenarios involving 25,168 faults and no faults conditions with different fault resistances and angles of fault inception, can classify all symmetrical non-symmetrical faults. The algorithm is tested on an IEEE 9-bus system with 32 samples per cycle sampling rate and is evaluated with ten-fold cross-validation and four evaluation metrics. This methodology provides an average accuracy of 99.89 per cent for all fault scenarios despite measurement noise having a signal-to-noise ratio of 30 dB, 35 dB, and 40 dB. The comparative analysis presented in this work reveals that the developed SVM model performs better than five other reported classification algorithms in terms of the accuracy of the classification of faults.