Advanced Fault Detection in Power Transformer using SFRA, Statistical Indicator and ANFIS

Advanced Fault Detection in Power Transformer using SFRA, Statistical Indicator and ANFIS | 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 Advanced Fault Detection in Power Transformer using SFRA, Statistical Indicator and ANFIS P Siva Kumar, Chandan Kumar, Vankadara Sampath Kumar, Saibal Chatterjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6172400/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Power transformers are vital components of electrical power systems, and their failure can lead to severe operational and economic consequences. Mechanical stresses, short circuits, and insulation aging contribute to transformer faults by inducing electromagnetic forces that result in axial and radial deformations. If left undetected, these deformations can weaken insulation integrity, reduce the transformer’s short-circuit endurance, and cause complete failure. To ensure reliability, an effective fault diagnosis method is required. sweep frequency response analysis (SFRA) is a powerful tool used to detect mechanical and electrical anomalies by comparing measured frequency responses with a healthy reference. This study utilizes SFRA in conjunction with mathematical tools such as lumped parameter models and an adaptive neuro-fuzzy inference system (ANFIS) to improve fault detection accuracy. ANFIS enables automated classification of transformer faults based on statistical indicators such as skewness, kurtosis, cross-correlation, and absolute difference sum (DABS). MATLAB-based simulations validate the effectiveness of the proposed approach by analyzing SFRA responses under various fault conditions. The results demonstrate that integrating machine learning with SFRA significantly enhances the precision of fault classification, making transformer health assessment more efficient. This research provides a step towards automated fault monitoring, reducing the reliance on manual expert interpretation and enhancing transformer lifespan. SFRA Power Transformer Fault Detection Machine Learning ANFIS Statistical Indicators Lumped Parameter Model Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor invited by journal 08 Jul, 2025 Editor assigned by journal 08 Mar, 2025 First submitted to journal 07 Mar, 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-6172400","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515651615,"identity":"e99582ee-afd3-44a5-abd5-9de1b30c5f14","order_by":0,"name":"P Siva Kumar","email":"","orcid":"","institution":"NIT Mizoram: National Institute of Technology Mizoram","correspondingAuthor":false,"prefix":"","firstName":"P","middleName":"Siva","lastName":"Kumar","suffix":""},{"id":515651616,"identity":"504d74f0-fc91-4fa3-be0a-5b4191a36c4b","order_by":1,"name":"Chandan Kumar","email":"","orcid":"","institution":"NIT Mizoram: National Institute of 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