High impedance fault detection in distribution Feeders using Extended Kalman Filter and Support Vector Machine

Abstract

The paper presents an intelligent technique for high impedance fault (HIF) detection using combined extended kalman filter (EKF) and supportvector machine (SVM). The proposed approach uses magnitude and phase change of fundamental, 3rd, 5th, 7th, 11th and 13th harmonic component as feature inputs to the SVM. The Gaussian kernel based SVM is trained with input sets each consists of ‘12’ features with corresponding target vector ‘1’ for HIF detection and ‘1’ for non-HIF condition. The magnitude and phase change are estimated using EKF. The proposed approach is trained with 300 data sets and tested for 200 data sets including wide variations in operating conditions and provides excellent results in noisy environment. Thus, the proposed method is found to be fast, accurate, and robust for HIF detection in distribution feeders.