Combined EKF and SVM based High Impedance Fault detection in power distribution feeders

The paper presents an intelligent technique for High Impedance Fault (HIF) detection using combined Extended Kalman Filter and Support Vector Machine. The proposed approach uses magnitude and phase change of fundamental, 3^rd, 5^th, 7^th, 11^th and 13^th 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 Extended Kalman Filter. 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.