基于均值聚类和支持向量机的电力变压器故障诊断组合技术

IF 3.8 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Nanodielectrics Pub Date : 2024-07-02 DOI:10.1049/nde2.12088
Arnaud Nanfak, Abdelmoumene Hechifa, Samuel Eke, Abdelaziz Lakehal, Charles Hubert Kom, Sherif S. M. Ghoneim
{"title":"基于均值聚类和支持向量机的电力变压器故障诊断组合技术","authors":"Arnaud Nanfak,&nbsp;Abdelmoumene Hechifa,&nbsp;Samuel Eke,&nbsp;Abdelaziz Lakehal,&nbsp;Charles Hubert Kom,&nbsp;Sherif S. M. Ghoneim","doi":"10.1049/nde2.12088","DOIUrl":null,"url":null,"abstract":"<p>This contribution presents a two-step hybrid diagnostic approach, combining <i>k</i>-means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.</p>","PeriodicalId":36855,"journal":{"name":"IET Nanodielectrics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/nde2.12088","citationCount":"0","resultStr":"{\"title\":\"A combined technique for power transformer fault diagnosis based on k-means clustering and support vector machine\",\"authors\":\"Arnaud Nanfak,&nbsp;Abdelmoumene Hechifa,&nbsp;Samuel Eke,&nbsp;Abdelaziz Lakehal,&nbsp;Charles Hubert Kom,&nbsp;Sherif S. M. Ghoneim\",\"doi\":\"10.1049/nde2.12088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This contribution presents a two-step hybrid diagnostic approach, combining <i>k</i>-means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.</p>\",\"PeriodicalId\":36855,\"journal\":{\"name\":\"IET Nanodielectrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/nde2.12088\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Nanodielectrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/nde2.12088\",\"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/nde2.12088","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

本文提出了一种两步混合诊断方法,结合了用于形成子集的 k-means 聚类,以及由人类专家进行的子集分析。由于特征输入向量对无监督机器学习算法的性能有重要影响,因此在子集形成阶段探讨了从传统方法中衍生出的七个特征输入向量,包括杜瓦尔五边形法、罗杰斯比率法、三比率技术、Denkyoken 法、集合气体特征法、杜瓦尔三角形法和高达三角形法。使用 595 个 DGA 样本数据集实现了与七个特征输入向量相对应的七种拟议的单独方法,并在另外 254 个 DGA 样本上进行了测试。此外,还引入了一种基于支持向量机的组合技术,利用单个方法的诊断结果作为输入特征。通过训练和测试,综合技术的诊断结果分别为 91.09% 和 90.94%,总体诊断准确率最高。利用 IEC TC10 数据库,将所提出的诊断方法的诊断准确率与现有的文献方法进行了比较。从获得的结果来看,组合技术优于所建议的单个方法和用于比较的现有方法。
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
A combined technique for power transformer fault diagnosis based on k-means clustering and support vector machine

This contribution presents a two-step hybrid diagnostic approach, combining k-means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

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