A Novel Fault Classification Scheme Based on Least Square SVM

TL;DR

This paper introduces a new fault classification method using least square support vector machines, effectively identifying faults in series compensated transmission lines with high accuracy even in noisy conditions.

Contribution

It proposes a novel LS-SVM based classification scheme with four binary classifiers for phase and ground fault detection in transmission lines.

Findings

High accuracy in fault classification

Reliable performance in noisy environments

Effective identification of phase and ground faults

Abstract

This paper presents a novel approach for fault classification and section identification in a series compensated transmission line based on least square support vector machine. The current signal corresponding to one-fourth of the post fault cycle is used as input to proposed modular LS-SVM classifier. The proposed scheme uses four binary classifier; three for selection of three phases and fourth for ground detection. The proposed classification scheme is found to be accurate and reliable in presence of noise as well. The simulation results validate the efficacy of proposed scheme for accurate classification of fault in a series compensated transmission line.