A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network

Abstract

This paper proposes an ensemble Random Subspace (RS) classifier for discrimination of High Impedance Fault (HIF) in photovoltaic connected power network. The design and simulation of power network is considered in MATLAB/Simulink environment to analyze various faults (HIF, Symmetrical, and unsymmetrical), and non-fault events. In pre-process stage of classification, the features from current signals of different events are extracted by using discrete wavelet transform technique. Then, features are used to learn the RS ensemble and base classifiers (K-nearest neighbor, Logistic regression, and Random tree) to get predictions in classification phase. The classification analysis is carried out under with and without real-time varying solar irradiance, and addition of noise data over the input of classifiers. The proposed RS ensemble classifier, discriminates HIF with higher accuracy and success rate than base classifiers. Further, the effectiveness was verified with evaluation of performance indices which shows the proffered ensemble classifier outperforms base classifiers.