Remaining useful life of power transformers using efficient surrogates and deep learning techniques

Remaining useful life of power transformers using efficient surrogates and deep learning techniques | 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 Research Article Remaining useful life of power transformers using efficient surrogates and deep learning techniques Lila ACHOUR, Sebastian Rodriguez, Paul-Henri Langlois, Hafid Fikri, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7602540/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The assessment of remaining useful life (RUL) in power transformers (PTs) is critical, given their essential role in the reliable distribution of electrical energy. According to international standards, the RUL is primarily influenced by the temperature of the hotspot (HST) located near the insulating paper that separates the coils from the mineral oil. Degradation of this insulating paper can lead to catastrophic failures, as its breakdown results in direct contact between the coil and the oil, potentially triggering explosions and abrupt transformer malfunctions. To solve this problem, this study presents a novel approach that addresses the limitations of traditionally simplified modeling frameworks by using a reduced model based on sparse Proper Generalized Decomposition (sPGD) of a three-dimensional high-fidelity convective model that provides predictions of the HST behavior in real time. This surrogate model utilizes extensive historical operational data including ambient temperature and transformer power consumption collected over several years to estimate the transformer’s time to failure. The proposed methodology demonstrates an impressive ability to evaluate the RUL of power transformers in less than 2 seconds by simulating the HST over 50 years, while faithfully reproducing the predictions derived from multiphysics and high-fidelity models. Furthermore, this study proposes a data-driven framework based on a Neural ODE for estimating the degradation function of the insulation paper of power transformers, relying solely on the known time of failure and historical operational data. The generated dataset allows the validation of this methodology, which enables the accurate estimation of both the loss of life of a transformer during its use cycle, as well as the estimation of the degradation function of the insulating paper. The framework provides a robust and generalizable approach for real-time health assessment and life cycle management of critical assets. Power Transformer Remaining useful life Model-order reduction Degradation law Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 04 Jan, 2026 Reviewers agreed at journal 16 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 12 Sep, 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-7602540","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518980127,"identity":"a8e8e1f8-ae4e-43a4-a07e-807684b95e2d","order_by":0,"name":"Lila 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techniques","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":"advanced-modeling-and-simulation-in-engineering-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amos","sideBox":"Learn more about [Advanced Modeling and Simulation in Engineering Sciences](http://amses-journal.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amos/default.aspx","title":"Advanced Modeling and Simulation in Engineering Sciences","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Power Transformer, Remaining useful life, Model-order reduction, Degradation law","lastPublishedDoi":"10.21203/rs.3.rs-7602540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7602540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe assessment of remaining useful life (RUL) in power transformers (PTs) is critical, given their essential role in the reliable distribution of electrical energy. According to international standards, the RUL is primarily influenced by the temperature of the hotspot (HST) located near the insulating paper that separates the coils from the mineral oil. Degradation of this insulating paper can lead to catastrophic failures, as its breakdown results in direct contact between the coil and the oil, potentially triggering explosions and abrupt transformer malfunctions. To solve this problem, this study presents a novel approach that addresses the limitations of traditionally simplified modeling frameworks by using a reduced model based on sparse Proper Generalized Decomposition (sPGD) of a three-dimensional high-fidelity convective model that provides predictions of the HST behavior in real time. This surrogate model utilizes extensive historical operational data including ambient temperature and transformer power consumption collected over several years to estimate the transformer\u0026rsquo;s time to failure. The proposed methodology demonstrates an impressive ability to evaluate the RUL of power transformers in less than 2 seconds by simulating the HST over 50 years, while faithfully reproducing the predictions derived from multiphysics and high-fidelity models. Furthermore, this study proposes a data-driven framework based on a Neural ODE for estimating the degradation function of the insulation paper of power transformers, relying solely on the known time of failure and historical operational data. The generated dataset allows the validation of this methodology, which enables the accurate estimation of both the loss of life of a transformer during its use cycle, as well as the estimation of the degradation function of the insulating paper. The framework provides a robust and generalizable approach for real-time health assessment and life cycle management of critical assets.\u003c/p\u003e","manuscriptTitle":"Remaining useful life of power transformers using efficient surrogates and deep learning techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 04:36:58","doi":"10.21203/rs.3.rs-7602540/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T09:10:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T13:04:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135596846981832147615577038251860970769","date":"2025-12-16T16:17:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T14:03:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98058994079123116432726093337940250811","date":"2025-11-19T08:14:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T22:17:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122133881157539940662043882619464725428","date":"2025-09-22T22:00:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T14:36:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T13:52:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T11:51:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Advanced Modeling and Simulation in Engineering Sciences","date":"2025-09-12T16:54:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"advanced-modeling-and-simulation-in-engineering-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amos","sideBox":"Learn more about [Advanced Modeling and Simulation in Engineering Sciences](http://amses-journal.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amos/default.aspx","title":"Advanced Modeling and Simulation in Engineering Sciences","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f554337-cde6-4036-a3aa-9a5f77aba03c","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T19:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 04:36:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7602540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7602540","identity":"rs-7602540","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}