Innovative diagnosis of transformer winding defects using fuzzy and neutrosophic cross entropy measures

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Advanced Engineering Informatics Pub Date : 2025-02-16 DOI:10.1016/j.aei.2025.103196
Ali Reza Abbasi , Chander Parkash
{"title":"Innovative diagnosis of transformer winding defects using fuzzy and neutrosophic cross entropy measures","authors":"Ali Reza Abbasi ,&nbsp;Chander Parkash","doi":"10.1016/j.aei.2025.103196","DOIUrl":null,"url":null,"abstract":"<div><div>Power transformers are critical components in electrical power systems, and their reliable operation is essential for the stability of power grids. This research introduces an innovative methodology for the diagnosis and taxonomy of transformer winding defects, leveraging fuzzy and neutrosophic cross entropy measures. Traditional transfer function (TF) analysis, while widely used, often depends on expert interpretation and is limited in detecting minor and incipient faults. To address these challenges, we propose the integration of fuzzy cross entropy measure (FCEM) and neutrosophic cross entropy measure (NCEM) with TF analysis. The methodology encompasses several critical steps: measuring the frequency response of transformer windings under various faults, normalizing the transfer function responses, extracting lower and upper bounds, and constructing fuzzy and neutrosophic sets of faulty and healthy conditions. Subsequently, cross entropy values between healthy and faulty conditions are computed to identify and classify defects. The proposed approach is validated through a real case study, demonstrating its effectiveness in automating fault detection and reducing reliance on expert knowledge. The results indicate that the highest cross entropy measure values accurately reflect the presence of transformer winding defects across different frequency bands. This innovative approach not only enhances the accuracy of fault detection but also greatly advances intelligent fault diagnosis in power transformers, offering a more reliable and user-friendly solution for maintaining the integrity of power systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103196"},"PeriodicalIF":8.0000,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034625000898","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Power transformers are critical components in electrical power systems, and their reliable operation is essential for the stability of power grids. This research introduces an innovative methodology for the diagnosis and taxonomy of transformer winding defects, leveraging fuzzy and neutrosophic cross entropy measures. Traditional transfer function (TF) analysis, while widely used, often depends on expert interpretation and is limited in detecting minor and incipient faults. To address these challenges, we propose the integration of fuzzy cross entropy measure (FCEM) and neutrosophic cross entropy measure (NCEM) with TF analysis. The methodology encompasses several critical steps: measuring the frequency response of transformer windings under various faults, normalizing the transfer function responses, extracting lower and upper bounds, and constructing fuzzy and neutrosophic sets of faulty and healthy conditions. Subsequently, cross entropy values between healthy and faulty conditions are computed to identify and classify defects. The proposed approach is validated through a real case study, demonstrating its effectiveness in automating fault detection and reducing reliance on expert knowledge. The results indicate that the highest cross entropy measure values accurately reflect the presence of transformer winding defects across different frequency bands. This innovative approach not only enhances the accuracy of fault detection but also greatly advances intelligent fault diagnosis in power transformers, offering a more reliable and user-friendly solution for maintaining the integrity of power 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
求助全文
约1分钟内获得全文 去求助
来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
期刊最新文献
Moving load induced dynamic response analysis of bridge based on physics-informed neural network Multivariate failure prognosis of cutting tools under heterogeneous operating conditions FD-LLM: Large language model for fault diagnosis of complex equipment SR-FABNet: Super-Resolution branch guided Fourier attention detection network for efficient optical inspection of nanoscale wafer defects An LLM-based knowledge and function-augmented approach for optimal design of remanufacturing process
×
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