Arc fault detection using artificial intelligence: Challenges and benefits-Reference-Cited by-同舟云学术

Arc fault detection using artificial intelligence: Challenges and benefits

Published:2023 Issue:7 Volume:20 Page:12404-12432
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Tian Chunpeng¹,Xu Zhaoyang²,Wang Lukun¹,Liu Yunjie³

Affiliation:

1. College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China

2. University of Cambridge, Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, England

3. School of Communication Engineering, Taishan College of Scienceand Technology, Taian 271038, China

Abstract

<abstract><p>This systematic review aims to investigate recent developments in the area of arc fault detection. The rising demand for electricity and concomitant expansion of energy systems has resulted in a heightened risk of arc faults and the likelihood of related fires, presenting a matter of considerable concern. To address this challenge, this review focuses on the role of artificial intelligence (AI) in arc fault detection, with the objective of illuminating its advantages and identifying current limitations. Through a meticulous literature selection process, a total of 63 articles were included in the final analysis. The findings of this review suggest that AI plays a significant role in enhancing the accuracy and speed of detection and allowing for customization to specific types of faults in arc fault detection. Simultaneously, three major challenges were also identified, including missed and false detections, the restricted application of neural networks and the paucity of relevant data. In conclusion, AI has exhibited tremendous potential for transforming the field of arc fault detection and holds substantial promise for enhancing electrical safety.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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