A Method for DC Arc Fault Detection, Classification and Mitigation in Electric Vehicles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Arc faults in the power distribution system of an electric vehicle (EV) can result in damage to cables and associated equipment, as well as threaten the safety of the occupants of the EV. On-board components can create differential background noise, which limits the reliability of current arc detection methods. Within this paper an accurate and real-time arc fault detection method is designed and verified. An arc fault data pipeline is designed, trained and validated by using experimental data of damaged EV cables. Weak-current signals are preprocessed through a two-stage filter, and then the number of continuous over-threshold windows in the wavelet transform result are collected to determine the occurrence of the series arc. Our results, also demonstrate an immunity to background noise with a 150ms detection time and 99% accuracy.

Original languageEnglish
Title of host publication3rd Global Power, Energy and Communication Conference (GPECOM 2021)
PublisherIEEE
Pages7-12
Number of pages6
ISBN (Electronic)9781665435123
DOIs
Publication statusPublished - 13 Nov 2021
Event3rd IEEE Global Power, Energy and Communication Conference 2021 - Virtual, Online, Turkey
Duration: 5 Oct 20218 Oct 2021

Conference

Conference3rd IEEE Global Power, Energy and Communication Conference 2021
Abbreviated titleGPECOM 2021
Country/TerritoryTurkey
CityVirtual, Online
Period5/10/218/10/21

Keywords

  • Arc Fault
  • Data Analysis
  • Electric Vehicle
  • Fault Detection
  • Wavelet Transform

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Mechanical Engineering

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