On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety

Abstract

Accidents caused by faults on overhead power lines have been more frequently reported under extreme weather conditions and may strongly threaten the safety and stability of the power grids, e.g., massive wildfires caused by the electrical arcs or lines getting in touch with vegetation, relay miss-operations, etc. It has been widely recognized that the electric safety concerns engendered by overhead line faults have to be timely and properly addressed to minimize the subsequent risks and damages. The existing monitoring devices and protective relays can barely detect high impedance faults (HIFs) and are unable to warn the system operators until serious abnormalities or damages are observed. Aiming at avoiding the damaging consequences of HIFs, an online monitoring system embedded with machine learning analytics is proposed that ensures a fast and accurate detection of HIFs in power systems. The performance of the proposed artificial intelligence engine is tested under a variety of simulated conditions and the numerical results demonstrate its efficacy and superiority over the state-of-the-art advancements.