Tian Chunpeng, Xu Zhaoyang, Wang Lukun, Liu Yunjie
College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China.
University of Cambridge, Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, England.
Math Biosci Eng. 2023 May 23;20(7):12404-12432. doi: 10.3934/mbe.2023552.
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.
本系统综述旨在研究电弧故障检测领域的最新进展。对电力需求的不断增长以及能源系统的相应扩张，导致电弧故障风险增加以及相关火灾发生的可能性增大，这成为一个备受关注的问题。为应对这一挑战，本综述聚焦于人工智能（AI）在电弧故障检测中的作用，旨在阐明其优势并识别当前的局限性。通过精心的文献筛选过程，最终分析纳入了63篇文章。本综述的结果表明，人工智能在提高电弧故障检测的准确性和速度以及针对特定类型故障进行定制方面发挥着重要作用。同时，还确定了三个主要挑战，包括漏检和误检、神经网络的应用受限以及相关数据的匮乏。总之，人工智能在改变电弧故障检测领域方面展现出巨大潜力，并在提高电气安全性方面具有很大的前景。
