A review of intelligent verification system distributiontautomationtterminalinal based on artificial intelligealgorithmsthms

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-10-16 DOI:10.1186/s13677-023-00527-2
Hongwei Li, Qiyuan Xu, Qilin Wang, Bin Tang
{"title":"A review of intelligent verification system distributiontautomationtterminalinal based on artificial intelligealgorithmsthms","authors":"Hongwei Li, Qiyuan Xu, Qilin Wang, Bin Tang","doi":"10.1186/s13677-023-00527-2","DOIUrl":null,"url":null,"abstract":"Abstract Artificial intelligence (AI) plays a key role in the distribution automation system (DAS). By using artificial intelligence technology, it is possible to intelligently verify and monitor distribution automation terminals, improve their safety and reliability, and reduce power system operating and maintenance costs. At present, researchers are exploring a variety of application methods and algorithms of the distribution automation terminal intelligent acceptance system based on artificial intelligence, such as machine learning, deep learning and expert systems, and have made significant progress. This paper comprehensively reviews the existing research on the application of artificial intelligence technology in distribution automation systems, including fault detection, network reconfiguration, load forecasting, and network security. It undertakes a thorough examination and summarization of the major research achievements in the field of distribution automation systems over the past few years, while also analyzing the challenges that this field confronts. Moreover, this study elaborates extensively on the diverse applications of AI technology within distribution automation systems, providing a detailed comparative analysis of various algorithms and methodologies from multiple classification perspectives. The primary aim of this endeavor is to furnish valuable insights for researchers and practitioners in this domain, thereby fostering the advancement and innovation of distribution automation systems.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"76 1","pages":"0"},"PeriodicalIF":3.7000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cloud Computing-Advances Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s13677-023-00527-2","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract Artificial intelligence (AI) plays a key role in the distribution automation system (DAS). By using artificial intelligence technology, it is possible to intelligently verify and monitor distribution automation terminals, improve their safety and reliability, and reduce power system operating and maintenance costs. At present, researchers are exploring a variety of application methods and algorithms of the distribution automation terminal intelligent acceptance system based on artificial intelligence, such as machine learning, deep learning and expert systems, and have made significant progress. This paper comprehensively reviews the existing research on the application of artificial intelligence technology in distribution automation systems, including fault detection, network reconfiguration, load forecasting, and network security. It undertakes a thorough examination and summarization of the major research achievements in the field of distribution automation systems over the past few years, while also analyzing the challenges that this field confronts. Moreover, this study elaborates extensively on the diverse applications of AI technology within distribution automation systems, providing a detailed comparative analysis of various algorithms and methodologies from multiple classification perspectives. The primary aim of this endeavor is to furnish valuable insights for researchers and practitioners in this domain, thereby fostering the advancement and innovation of distribution automation systems.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于人工智能算法的智能验证系统分布自动化终端研究综述
摘要人工智能(AI)在配电自动化系统(DAS)中起着关键作用。利用人工智能技术,可以对配电自动化终端进行智能验证和监控,提高其安全性和可靠性,降低电力系统运维成本。目前,研究人员正在探索基于机器学习、深度学习、专家系统等人工智能的配电自动化终端智能验收系统的多种应用方法和算法,并取得了重大进展。本文全面综述了人工智能技术在配电自动化系统中的应用研究,包括故障检测、网络重构、负荷预测和网络安全。对近年来配电自动化领域的主要研究成果进行了全面的考察和总结,同时分析了该领域面临的挑战。此外,本研究广泛阐述了人工智能技术在配电自动化系统中的各种应用,从多个分类角度对各种算法和方法进行了详细的比较分析。这项工作的主要目的是为该领域的研究人员和实践者提供有价值的见解,从而促进配电自动化系统的进步和创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Cloud Computing-Advances Systems and Applications
Journal of Cloud Computing-Advances Systems and Applications Computer Science-Computer Networks and Communications
CiteScore
6.80
自引率
7.50%
发文量
76
审稿时长
75 days
期刊介绍: The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. Comprehensive review and survey articles that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.
期刊最新文献
Research on electromagnetic vibration energy harvester for cloud-edge-end collaborative architecture in power grid FedEem: a fairness-based asynchronous federated learning mechanism Adaptive device sampling and deadline determination for cloud-based heterogeneous federated learning Review on the application of cloud computing in the sports industry Improving cloud storage and privacy security for digital twin based medical records
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1