Enhanced Rapid Detection of High-impedance Arc Faults in Medium Voltage Electrical Distribution Networks

TL;DR

This paper introduces a rapid, interpretable method using HAVOK analysis for detecting high-impedance arc faults in medium-voltage power systems, significantly improving detection speed and robustness over previous techniques.

Contribution

The study develops a novel HAVOK-based approach for medium-voltage arc fault detection, addressing a gap in existing low-voltage focused research and enabling real-time, reliable identification.

Findings

Detection speed improved to 0.45 ms

Achieved 99.36% accuracy over traditional methods

Effective across various grounding and noise scenarios

Abstract

High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety when grounded with different surfaces. Previous research has concentrated predominantly on arc fault detection in low-voltage systems, leaving a significant gap in medium-voltage applications. In this work, a novel approach has been developed that enables rapid arc fault detection for medium-voltage distribution lines. In contrast to existing black-box feature-based approaches, the Hankel alternative view of the Koopman (HAVOK) analysis developed from nonlinear dynamics has been applied, which not only offers interpretable features but also opens up new application options in the area of arc fault detection. The method achieves a much faster detection speed in 0.45 ms, 99.36\% enhanced compared to harmonic randomness and waveform distortion method, thus making it suitable for real-time applications. It demonstrates the ability to detect arc faults across various scenarios, including different grounding surfaces and levels of system noise, boosting its practical importance for stakeholders in safety-critical industries.