Flexible DC distribution network fault detection method based on MTF-EfficientNetV2 algorithm

Flexible DC distribution network fault detection method based on MTF-EfficientNetV2 algorithm | 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 Flexible DC distribution network fault detection method based on MTF-EfficientNetV2 algorithm Zhi-hui Zeng, Jia-yin Li, Yan-fang Wei, Xiao-wei Wang, Ying-ying Zheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4639356/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 Given the swift advancement of clean energy, flexible DC distribution network has become a research hotspot for future power grids. Existing DC line fault detection methods have problems such as low detection precision and vulnerability to resistance. For this reason, a fault detection method built on the upgraded EfficientNetV2 algorithm is proposed. Primarily, the fault transient voltage time-domain data are gathered. To enhance the variability of fault features, the data are transformed to a two-dimensional image by Markov variation field. Then, a dual-channel attention mechanism is used to shortlist and fuse the features with channel and spatial features, respectively. Finally, the fused features are fed into EfficientNetV2 for training. And the detection results are obtained by testing the model under different working conditions. The findings demonstrate the excellent detection accuracy of the approach. The average accuracy can reach 98.95%. Flexible DC distribution network EfficientNetV2 Attention mechanism Markov variation field Fault detection 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-4639356","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327431532,"identity":"a7e9d2f6-2a4f-48e1-9f34-e8c5e783a1e1","order_by":0,"name":"Zhi-hui Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFACHjaGjw1glgHxWhhnkqyFmZckLfL9Z489tt1xJ7GBvXmbBEPNHcJaGBvOpRvnnnmW2MBzrEyC4dgzwlqYGXvMpHPbDic2SOSYSTA2HCashY2Zx0zaEqRF/g2RWnjYgFoYwbbwEKlFgofHTLK37ZlxG09asUXCMSK0yPefMZP42XZHtp/98MYbH2qI0AIFBxjYQFQC0RpAWkbBKBgFo2AU4AQAZio1D5VqLlMAAAAASUVORK5CYII=","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Zhi-hui","middleName":"","lastName":"Zeng","suffix":""},{"id":327431533,"identity":"1299f287-701f-4d40-a3ea-b9db200696a9","order_by":1,"name":"Jia-yin Li","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Jia-yin","middleName":"","lastName":"Li","suffix":""},{"id":327431534,"identity":"0f58e637-9076-4f60-9ed9-5432715baa39","order_by":2,"name":"Yan-fang Wei","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yan-fang","middleName":"","lastName":"Wei","suffix":""},{"id":327431535,"identity":"d34e39ef-5d89-48df-a0bb-ad5410cfa99d","order_by":3,"name":"Xiao-wei Wang","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiao-wei","middleName":"","lastName":"Wang","suffix":""},{"id":327431537,"identity":"f35e2b55-8ffe-4ae6-9da6-643409ce298b","order_by":4,"name":"Ying-ying Zheng","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Ying-ying","middleName":"","lastName":"Zheng","suffix":""},{"id":327431539,"identity":"777b0601-f61b-4d28-9150-6106e524e0b1","order_by":5,"name":"Yu-hai Zhang","email":"","orcid":"","institution":"Henan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yu-hai","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-26 02:32:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4639356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4639356/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69728448,"identity":"0b9c8f0e-7474-4f35-9503-45ecc723c23b","added_by":"auto","created_at":"2024-11-24 11:31:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":655452,"visible":true,"origin":"","legend":"","description":"","filename":"FlexibleDCdistributionnetworkfaultdetectionmethodbasedonMTFEfficientNetV2algorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4639356/v1_covered_6b2b47c2-5ced-431a-83f2-04fcb32c1046.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Flexible DC distribution network fault detection method based on MTF-EfficientNetV2 algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Flexible DC distribution network, EfficientNetV2, Attention mechanism, Markov variation field, Fault detection","lastPublishedDoi":"10.21203/rs.3.rs-4639356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4639356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGiven the swift advancement of clean energy, flexible DC distribution network has become a research hotspot for future power grids. 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