New swing-blocking methods for digital distance protection using Support Vector Machine

Abstract

This paper presents a method for power swing and fault diagnosis of power system based on Support Vector Machine (SVM) classifier. The method adopts Least Square Support Vector Machine (LS-SVM) classifier to identify the power swing and fault types. The power swing blocking elements are based on monitor the rate of change of the impedance, the power swing center voltage, the positive current and zero sequence component. The process of training the LS-SVM using a K-folded cross validation process for determining the values of parameter σ and parameter λ in RBF kernel parameter that will give minimum classification error. The proposed method can successfully detect power swing and provide power swing blocking for accurate distance protection during power swing.