Expert system for power transformer diagnosis

R. Velásquez, Jennifer Vanessa Mejía Lara
{"title":"Expert system for power transformer diagnosis","authors":"R. Velásquez, Jennifer Vanessa Mejía Lara","doi":"10.1109/INTERCON.2017.8079640","DOIUrl":null,"url":null,"abstract":"Power transformers are the most critical part of power electrical system. The oil and the insulation system are subjected to degradation for many chemicals inside them, they are the result of an initial problem that can be predicted. In this research, the intelligent diagnosis system based on component analysis (PCA) and adaptive decision system based on fuzzy logic permits to realize a dissolved gas analysis (DGA) [1] to predict incipient fault diagnosis by different methods, to obtain deterioration rates and health index, besides it allows to analyze the degree of polymerization (DP) for the remaining life of the equipment. It determinates the comparative rates and determinate the efficiency and effective of this diagnosis system, it improves the international standards IEEE, IEC, among others. The correct diagnosis performance of the PCA and fuzzy logic is calculated on 107 samples on Peruvian power electrical systems with excellent results.","PeriodicalId":229086,"journal":{"name":"2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTERCON.2017.8079640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

Power transformers are the most critical part of power electrical system. The oil and the insulation system are subjected to degradation for many chemicals inside them, they are the result of an initial problem that can be predicted. In this research, the intelligent diagnosis system based on component analysis (PCA) and adaptive decision system based on fuzzy logic permits to realize a dissolved gas analysis (DGA) [1] to predict incipient fault diagnosis by different methods, to obtain deterioration rates and health index, besides it allows to analyze the degree of polymerization (DP) for the remaining life of the equipment. It determinates the comparative rates and determinate the efficiency and effective of this diagnosis system, it improves the international standards IEEE, IEC, among others. The correct diagnosis performance of the PCA and fuzzy logic is calculated on 107 samples on Peruvian power electrical systems with excellent results.
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
电力变压器诊断专家系统
电力变压器是电力系统中最关键的部件。油和绝缘系统受到许多化学物质的降解,它们是一个可以预测的初始问题的结果。在本研究中,基于成分分析(PCA)的智能诊断系统和基于模糊逻辑的自适应决策系统可以实现溶解气体分析(DGA)[1],通过不同的方法预测早期故障诊断,获得劣化率和健康指数,并可以分析设备剩余寿命的聚合度(DP)。确定了比较率,确定了诊断系统的效率和有效性,提高了IEEE、IEC等国际标准。在秘鲁电力系统的107个样本上计算了主成分分析和模糊逻辑的正确诊断性能,取得了良好的结果。
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
自引率
0.00%
发文量
0
期刊最新文献
Evaluating informatics security in an organization: The minimal distance method Comparison of wavelet transform symlets (2-10) and daubechies (2-10) for an electroencephalographic signal analysis Nonlinear modeling of magnetic materials for electromagnetic devices simulation Integration of IT frameworks for the management of information security within industrial control systems providing metrics and indicators V/F control of an induction motor with THD optimization using cascaded multilevel converters
×
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