Modified SVR Prediction Method of Transient EMI based on Theoretical Simulation and Experimental Analysis

Modified SVR Prediction Method of Transient EMI based on Theoretical Simulation and Experimental Analysis | 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 Modified SVR Prediction Method of Transient EMI based on Theoretical Simulation and Experimental Analysis Bin Xu, Zhengxin Gao, Jianyong Zheng, Shoucheng Ji, Xuhui Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7498279/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 The very-fast-transient-overvoltage(VFTO) generated by the operation of gas-insulated switchgear (GIS) is an important threat to the secondary-side equipment. In order to investigate the generation mechanism, a distributed parameter model by ATP-EMTP software was established to simulate the transient EMI. On the basis of theoretical simulation, an experiment was addressed to test EMI of secondary-side equipment. The test results indicated that theoretical simulation was reliable and the interference experienced by the secondary-side equipment was inversely proportional to the cable length, in addition Spearman rank correlation method was adopted to analyze the linear correlation between interference voltage at the secondary side and the breakdown voltage at the primary side. A modified support vector regression method was proposed to conduct quantitative analysis and prediction of the maximum interference level at the secondary side. By comparison with the practical tests under a 220kV GIS benchmark, the predicted precision of proposed method could achieve 98.6%. This investigation could provide technical support for EMI testing, analysis, and suppression of integrated primary and secondary equipment in substations. electromagnetic interference gas-insulated switchgear spearman rank correlation support vector regression model 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-7498279","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534933163,"identity":"d129e0dc-875c-4c57-a52d-b8a1cf2f6c94","order_by":0,"name":"Bin 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