Abstract
The paper presents a new technique for high impedance fault (HIF) detection in power distribution network using ensemble decision trees (random forest). Giving the randomness in the ensemble of decision trees (DT) stacked inside the random forest (RF) model, it provides effective decision on HIF detection. The process starts with estimating the amplitude and phase of harmonic contents (fundamental, 3rd, 5th, 7th, 11th and 13th) in the HIF current signal using Extended Kalman Filter (EKF). In the next stage, random forest is trained with the amplitude and phase information of the HIF current signal to build up a highly efficient classifier for HIF detection. While testing, the proposed RF based classifier provides HIF detection with more than 99% reliability, considering extreme operating conditions of the power distribution network. The results indicate that the proposed method can reliably detect HIF in large power distribution network.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Electrical Power & Energy Systems
Disclaimer: 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.