Research on digital twin diagnosis model for the thermal-electric field of high-voltage switchgears

Research on digital twin diagnosis model for the thermal-electric field of high-voltage switchgears | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on digital twin diagnosis model for the thermal-electric field of high-voltage switchgears Feng Ding, Yongji Ma, Xinjue Li, Jiaqi Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6651594/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract High-voltage switchgear is a critical component in modern power systems, yet it remains vulnerable to insulation degradation and other faults under complex operating conditions. To address these challenges, a digital twin-based online fault diagnosis method is proposed for high-voltage switchgear, integrating thermal and electric field analysis. A three-dimensional model of the KYN28-12(Z) switchgear is first established, incorporating multi-physics simulations to identify key monitoring regions. Building on this, a digital twin surrogate and information model are developed to enable real-time reconstruction and online characterization of coupled thermal-electric fields. To extract fault features, optimized classification tree (OCT) and random forest algorithms are employed, while an enhanced adaptive neural-fuzzy inference system (ANFIS) is constructed for intelligent fault diagnosis. This approach enables accurate virtual-real mapping of switchgear states and provides theoretical support for intelligent operation and maintenance. Experimental results demonstrate a fault recognition rate of 93.4%, with only a 2.3% accuracy drop under 30% noise, verifying the robustness and reliability of the proposed method. Physical sciences/Engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Energy infrastructure High-voltage switchgear Digital twin Finite element simulation Fault diagnosis Surrogate model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers invited by journal 01 Jul, 2025 Editor invited by journal 01 Jul, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 21 May, 2025 First submitted to journal 21 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6651594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":479417830,"identity":"5f3253d9-221f-43ce-bb34-5acce788bb74","order_by":0,"name":"Feng Ding","email":"","orcid":"","institution":"Institute of Rail Transit, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Ding","suffix":""},{"id":479417831,"identity":"ef3c7dd1-47e0-4106-b7fe-4341fa71387a","order_by":1,"name":"Yongji Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACPoYEIFlRY8fY3gBkGFgQ1sIG1nLmWDJzzwGQFgkitTC2MDO2zwDpZSBGC3uO4efCBjZm3pnPr274USDBwN/enYBfC88bY+mZO2T4JGfnlN3sATpM4szZDfi1SOQYSPOeYWM2nJ2TdoMHqMVAIpegFuPfvG3MjPtvnkm7+YdILWbSIC2NM9iP3SbOFp5nZdY8wEBm7Mlhuy1jIMFD0C/87Mmbb/OAo/L4s5tv/tjI8bf34teCBHgMwCSxykGA/QEpqkfBKBgFo2AEAQC9mEL34rtyJQAAAABJRU5ErkJggg==","orcid":"","institution":"College of Electrical Engineering, Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Yongji","middleName":"","lastName":"Ma","suffix":""},{"id":479417833,"identity":"6aae14dd-c853-4b7d-b9ad-fd1d67633b0e","order_by":2,"name":"Xinjue Li","email":"","orcid":"","institution":"Shanghai Marine Equipment Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Xinjue","middleName":"","lastName":"Li","suffix":""},{"id":479417835,"identity":"5d6fcf16-e7bf-4bdc-9855-105051368616","order_by":3,"name":"Jiaqi Huang","email":"","orcid":"","institution":"Shanghai Marine Equipment Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-05-13 05:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6651594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6651594/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-15626-0","type":"published","date":"2025-10-01T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92883785,"identity":"fa940c4e-cb54-4bf3-b30f-4b648d60aefb","added_by":"auto","created_at":"2025-10-06 16:09:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1117537,"visible":true,"origin":"","legend":"","description":"","filename":"Researchondigitaltwindiagnosismodelforthethermalelectricfieldofhighvoltageswitchgears.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6651594/v1_covered_8b6950cb-f2ef-4e5f-9429-203d5ea0d702.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on digital twin diagnosis model for the thermal-electric field of high-voltage switchgears","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"High-voltage switchgear, Digital twin, Finite element simulation, Fault diagnosis, Surrogate model","lastPublishedDoi":"10.21203/rs.3.rs-6651594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6651594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-voltage switchgear is a critical component in modern power systems, yet it remains vulnerable to insulation degradation and other faults under complex operating conditions. To address these challenges, a digital twin-based online fault diagnosis method is proposed for high-voltage switchgear, integrating thermal and electric field analysis. A three-dimensional model of the KYN28-12(Z) switchgear is first established, incorporating multi-physics simulations to identify key monitoring regions. Building on this, a digital twin surrogate and information model are developed to enable real-time reconstruction and online characterization of coupled thermal-electric fields. To extract fault features, optimized classification tree (OCT) and random forest algorithms are employed, while an enhanced adaptive neural-fuzzy inference system (ANFIS) is constructed for intelligent fault diagnosis. This approach enables accurate virtual-real mapping of switchgear states and provides theoretical support for intelligent operation and maintenance. Experimental results demonstrate a fault recognition rate of 93.4%, with only a 2.3% accuracy drop under 30% noise, verifying the robustness and reliability of the proposed method.\u003c/p\u003e","manuscriptTitle":"Research on digital twin diagnosis model for the thermal-electric field of high-voltage switchgears","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 06:57:43","doi":"10.21203/rs.3.rs-6651594/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T06:30:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T07:57:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T15:03:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T13:30:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227092320807490831148218983847090928703","date":"2025-07-07T03:04:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197622938900010852531299394585501752107","date":"2025-07-04T05:14:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306787532799483923964009015214406964795","date":"2025-07-02T01:19:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-02T01:03:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-01T16:16:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T06:46:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-21T15:37:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-21T15:36:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"608e222d-c8f4-41be-8865-516af868025d","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50904962,"name":"Physical sciences/Engineering"},{"id":50904963,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":50904964,"name":"Physical sciences/Engineering/Energy infrastructure"}],"tags":[],"updatedAt":"2025-10-06T16:03:21+00:00","versionOfRecord":{"articleIdentity":"rs-6651594","link":"https://doi.org/10.1038/s41598-025-15626-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-01 15:57:56","publishedOnDateReadable":"October 1st, 2025"},"versionCreatedAt":"2025-07-03 06:57:43","video":"","vorDoi":"10.1038/s41598-025-15626-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-15626-0","workflowStages":[]},"version":"v1","identity":"rs-6651594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6651594","identity":"rs-6651594","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}