基于溶解气体分析的矿物油浸式电力变压器传统故障诊断方法:过去、现在和未来

IF 3.8 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Nanodielectrics Pub Date : 2024-04-22 DOI:10.1049/nde2.12082
Arnaud Nanfak, Eke Samuel, Issouf Fofana, Fethi Meghnefi, Martial Gildas Ngaleu, Charles Hubert Kom
{"title":"基于溶解气体分析的矿物油浸式电力变压器传统故障诊断方法:过去、现在和未来","authors":"Arnaud Nanfak,&nbsp;Eke Samuel,&nbsp;Issouf Fofana,&nbsp;Fethi Meghnefi,&nbsp;Martial Gildas Ngaleu,&nbsp;Charles Hubert Kom","doi":"10.1049/nde2.12082","DOIUrl":null,"url":null,"abstract":"<p>A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent-based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA-based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA-based methods for mineral oil-immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA-based diagnostic methods are also presented.</p>","PeriodicalId":36855,"journal":{"name":"IET Nanodielectrics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/nde2.12082","citationCount":"0","resultStr":"{\"title\":\"Traditional fault diagnosis methods for mineral oil-immersed power transformer based on dissolved gas analysis: Past, present and future\",\"authors\":\"Arnaud Nanfak,&nbsp;Eke Samuel,&nbsp;Issouf Fofana,&nbsp;Fethi Meghnefi,&nbsp;Martial Gildas Ngaleu,&nbsp;Charles Hubert Kom\",\"doi\":\"10.1049/nde2.12082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent-based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA-based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA-based methods for mineral oil-immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA-based diagnostic methods are also presented.</p>\",\"PeriodicalId\":36855,\"journal\":{\"name\":\"IET Nanodielectrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/nde2.12082\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Nanodielectrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/nde2.12082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Nanodielectrics","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/nde2.12082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

确保电力变压器高效安全运行的一个关键因素是及早准确地诊断出初期故障。在可用于实现这一目标的工具中,溶解气体分析 (DGA) 被电力变压器维护专业人员广泛使用。它是一种预防性维护工具,用于状态监测、故障诊断和意外停电预防。随着人工智能(AI)的发展,文献中提出了许多使用人工智能工具的基于智能的 DGA 数据解读方法。虽然这些方法能达到很高的诊断精度并提高 DGA 效率,但它们一般都很复杂,而且这些出版物中记录的研究很难复制。传统的基于 DGA 的方法简单、易于理解和实施,被电力变压器维护专业人员广泛使用。近年来提出的许多方法克服了先驱方法的局限性,而且越来越有效。作者对基于 DGA 的矿物油浸式电力变压器故障诊断传统方法进行了详细而全面的文献综述。这篇综述还探讨了如何提高现有传统方法的效率。此外,还介绍了提高基于 DGA 诊断方法效率所需注意的一些误区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Traditional fault diagnosis methods for mineral oil-immersed power transformer based on dissolved gas analysis: Past, present and future

A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent-based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA-based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA-based methods for mineral oil-immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA-based diagnostic methods are also presented.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IET Nanodielectrics
IET Nanodielectrics Materials Science-Materials Chemistry
CiteScore
5.60
自引率
3.70%
发文量
7
审稿时长
21 weeks
期刊最新文献
A combined technique for power transformer fault diagnosis based on k-means clustering and support vector machine Stability of giant dielectric properties in co‐doped rutile TiO2 ceramics under temperature and humidity High‐performance sulphur dioxide sensor: Unveiling the potential of photonic crystal fibre technology Improvement in non-linear electrical conductivity of silicone rubber by incorporating zinc oxide fillers and grafting small polar molecules Traditional fault diagnosis methods for mineral oil-immersed power transformer based on dissolved gas analysis: Past, present and future
×
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