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 language | English |
|---|---|
| Title of host publication | 3rd Global Power, Energy and Communication Conference (GPECOM 2021) |
| Publisher | IEEE |
| Pages | 7-12 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665435123 |
| DOIs | |
| Publication status | Published - 13 Nov 2021 |
| Event | 3rd IEEE Global Power, Energy and Communication Conference 2021 - Virtual, Online, Turkey Duration: 5 Oct 2021 → 8 Oct 2021 |
Conference
| Conference | 3rd IEEE Global Power, Energy and Communication Conference 2021 |
|---|---|
| Abbreviated title | GPECOM 2021 |
| Country/Territory | Turkey |
| City | Virtual, Online |
| Period | 5/10/21 → 8/10/21 |
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
