Machine learning assessment of IoT managed microgrid protection in existence of SVC using wavelet methodology

Q3 Engineering
K. Lakshmi, P. Panigrahi, R. Goli
{"title":"Machine learning assessment of IoT managed microgrid protection in existence of SVC using wavelet methodology","authors":"K. Lakshmi, P. Panigrahi, R. Goli","doi":"10.3934/electreng.2022022","DOIUrl":null,"url":null,"abstract":"In the last decade, research has been started due to accelerated growth in power demand has mainly concentrated on the large power production and quality of power. After the digital revolution, non-conventional energy sources, many state-of-art equipment, power electronics loads, reactive power compensating devices, sophisticated measuring devices, etc., entered the power industry. The reactive power compensating devices, connected electrical equipment, renewable energy sources can be anticipated/unanticipated action can cause considerable reactions may be failure issues to power grids. To deal with these challenges, the power sector crucially needs to design and implement new security systems to protect its systems. The Internet-of-Things (IoT) is treated as revolution technology after the invention of the digital machine and the internet. New developments in sensor devices with wireless technologies through embedded processors provide effective monitoring and different types of faults can be detected during electric power transmission. The wavelet (WT) is one of the mathematical tools to asses transient signals of different frequencies and provides crucial information in the form of detailed coefficients. Machine learning (ML) methods are recommended in the power systems community to simplify digital reform. ML and AI techniques can make effective and rapid decisions to improve the stability and safety of the power grid. This recommended approach can contribute critical information about symmetrical or asymmetrical faults through machine learning assessment of IoT supervised microgrid protection in the presence of SVC using the wavelet approach covers diversified types of faults combined with fault-inception-angles (FIA).","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIMS Electronics and Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3934/electreng.2022022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

In the last decade, research has been started due to accelerated growth in power demand has mainly concentrated on the large power production and quality of power. After the digital revolution, non-conventional energy sources, many state-of-art equipment, power electronics loads, reactive power compensating devices, sophisticated measuring devices, etc., entered the power industry. The reactive power compensating devices, connected electrical equipment, renewable energy sources can be anticipated/unanticipated action can cause considerable reactions may be failure issues to power grids. To deal with these challenges, the power sector crucially needs to design and implement new security systems to protect its systems. The Internet-of-Things (IoT) is treated as revolution technology after the invention of the digital machine and the internet. New developments in sensor devices with wireless technologies through embedded processors provide effective monitoring and different types of faults can be detected during electric power transmission. The wavelet (WT) is one of the mathematical tools to asses transient signals of different frequencies and provides crucial information in the form of detailed coefficients. Machine learning (ML) methods are recommended in the power systems community to simplify digital reform. ML and AI techniques can make effective and rapid decisions to improve the stability and safety of the power grid. This recommended approach can contribute critical information about symmetrical or asymmetrical faults through machine learning assessment of IoT supervised microgrid protection in the presence of SVC using the wavelet approach covers diversified types of faults combined with fault-inception-angles (FIA).
利用小波方法对存在SVC的物联网管理微电网保护进行机器学习评估
近十年来,由于电力需求的加速增长,研究已经开始,主要集中在电力生产和电力质量上。数字革命后,非常规能源、许多先进设备、电力电子负载、无功补偿装置、精密测量装置等进入电力行业。无功补偿装置、连接的电气设备、可再生能源可预见/不可预见的动作可引起相当大的反应,可能是电网故障问题。为了应对这些挑战,电力部门迫切需要设计和实施新的安全系统来保护其系统。物联网(IoT)被视为继数字机器和互联网之后的革命性技术。通过嵌入式处理器,无线技术传感器设备的新发展提供了有效的监测,可以检测电力传输过程中不同类型的故障。小波是一种评估不同频率暂态信号的数学工具,它以详细系数的形式提供了关键信息。电力系统界推荐使用机器学习(ML)方法来简化数字化改革。机器学习和人工智能技术可以做出有效和快速的决策,以提高电网的稳定性和安全性。这种推荐的方法可以通过使用小波方法对存在SVC的物联网监督微电网保护进行机器学习评估,从而提供有关对称或不对称故障的关键信息,该方法涵盖了多种类型的故障,并结合了故障启动角(FIA)。
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 求助全文
来源期刊
AIMS Electronics and Electrical Engineering
AIMS Electronics and Electrical Engineering Engineering-Control and Systems Engineering
CiteScore
2.40
自引率
0.00%
发文量
19
审稿时长
8 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信