Deep learning for high-impedance fault detection and classification: transformer-CNN
Author:
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07219-z.pdf
Reference35 articles.
1. Wang B, Geng J, Dong X (2018) High-impedance fault detection based on nonlinear voltage-current characteristic profile identification. IEEE Trans Smart Grid 9(4):3783–3791
2. Gautam S, Brahma SM (2013) Detection of high impedance fault in power distribution systems using mathematical morphology. IEEE Trans Power Syst 28(2):1226–1234
3. Wei M, Liu W, Zhang H, Shi F, Chen W (2021) Distortion-based detection of high impedance fault in distribution systems. IEEE Trans Power Deliv 36(3):1603–1618
4. Yeh H-G, Sim S, Bravo RJ (2019) Wavelet and denoising techniques for real-time HIF detection in 12-kv distribution circuits. IEEE Syst J 13(4):4365–4373
5. Wang S, Dehghanian P (2020) On the use of artificial intelligence for high impedance fault detection and electrical safety. IEEE Trans Ind Appl 56(6):7208–7216
Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. An improved high-impedance fault identification scheme for distribution networks based on kernel extreme learning machine;International Journal of Electrical Power & Energy Systems;2024-01
2. A Critical Analysis on Different High Impedance Fault Detection Schemes;Electric Power Components and Systems;2023-11-22
3. High Impedance Fault Detection in Distribution Feeder Based on Maximum Overlap Discrete Wavelet Transform;2023 International Conference on Engineering, Science and Advanced Technology (ICESAT);2023-06-21
4. Using Deep Transfer Learning Technique to Protect Electrical Distribution Systems Against High-Impedance Faults;IEEE Systems Journal;2023-06
5. High Impedance Fault Detection in Distribution Networks using Unsupervised Learning Technique;2023 2nd International Conference for Innovation in Technology (INOCON);2023-03-03
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3