Abstract
AbstractThe paper presents an intelligent technique for high impedance fault (HIF) detection using combined extended kalman filter (EKF) and support vector 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. Copyright © 2009 John Wiley & Sons, Ltd.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
