State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index

State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index | 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 State Evaluation and Tripping Probability Prediction of Large Power Transformer Based on Health Index Zhongyang Xu, Lei Zhang, Ben Zhang, Tianjiao Qiao, Guiqing Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3852891/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aiming at the problem of overmaintenance and undermaintenance caused by lack of equipment running state, a method of state evaluation and tripping probability prediction of large power transformer based on health index is proposed. The paper constructs a transformer health evaluation system with a four layer deep architecture, uses the extension cloud theory to evaluate the deterioration of state indicators, combines the Analytic Hierarchy Process and Entropy Weight Method to weight the indicators in indicator level, introduces the improved Dezert-Smarandache (DSmT) theory to effectively integrate the evaluation results of each layer, and reconciles the contradictions and conflicts between conclusions. Using the health index to represent the health state of transformers, constructing a tripping probability prediction model with the health index as input, and obtaining the tripping probability of transformers. The research results indicate that the method proposed in this paper can accurately and effectively evaluate the health state of transformers and their functional components, providing information for the management and maintenance strategy of equipment. Information uncertainty State evaluation Health index Tripping probability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-3852891","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266661021,"identity":"ff4ef873-7d7d-4ce4-9427-94ed28a03ade","order_by":0,"name":"Zhongyang 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The paper constructs a transformer health evaluation system with a four layer deep architecture, uses the extension cloud theory to evaluate the deterioration of state indicators, combines the Analytic Hierarchy Process and Entropy Weight Method to weight the indicators in indicator level, introduces the improved Dezert-Smarandache (DSmT) theory to effectively integrate the evaluation results of each layer, and reconciles the contradictions and conflicts between conclusions. Using the health index to represent the health state of transformers, constructing a tripping probability prediction model with the health index as input, and obtaining the tripping probability of transformers. 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