Quantum mechanics

Engineering

Fault detection and isolation

Reliability engineering

Overhead (engineering)

High impedance

Protective relay

Actuator

Focused Impedance Measurement

Damages

Relay

Telecommunications

Power (physics)

Computer science

Fault (geology)

Seismology

Geology

Electrical impedance

Physics

Political science

Warning system

Electric power system

Power-system protection

Digital protective relay

Electrical engineering

IEEE Transactions on Industry Applications

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.

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