Ablation detection method of high-voltage cable buffer layer based on waveform characteristics and convolutional neural network

Ablation detection method of high-voltage cable buffer layer based on waveform characteristics and convolutional neural network | 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 Ablation detection method of high-voltage cable buffer layer based on waveform characteristics and convolutional neural network Kang Guo, Zhengping Wang, Zhibo Tian, Qian Li, Siying Wang, Zhenwei Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7711780/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 XLPE cables have been widely applied due to their safety and reliability, but in recent years, buffer layer ablation faults have frequently occurred. Because the ablation of the buffer layer develops slowly and is not easily detectable, timely and accurate detection of ablation is currently the focus of research. The simulation model of the cable is constructed based on the transmission line theory and the distributed parameter model. The characteristics of the frequency-domain impedance and phase waveform at the beginning of the cable are explored, and it is found that they have the characteristics of continuously decreasing with the increase of frequency and periodic changes. However, it is difficult to distinguish the ablation degree and location of the buffer layer only by analyzing the waveform characteristics. Convolutional neural networks can effectively extract data information and make corresponding judgments. In this study, based on the waveform characteristics of the cables, probabilistic clustering processing is conducted on the features, and then the data information is analyzed in combination with the convolutional neural network. The results show that this detection method can achieve an inspection with an accuracy rate of 0.95. It is a new method that can detect the ablation of the buffer layer in a timely and accurate manner without disassembling the cable. XLPE cables Buffer layer ablation Waveform characteristics Convolutional neural network Detection method 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. 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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-7711780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535317231,"identity":"47ee0252-1533-47a8-b969-d96dac7d2805","order_by":0,"name":"Kang 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network","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":"XLPE cables, Buffer layer ablation, Waveform characteristics, Convolutional neural network, Detection 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Because the ablation of the buffer layer develops slowly and is not easily detectable, timely and accurate detection of ablation is currently the focus of research. The simulation model of the cable is constructed based on the transmission line theory and the distributed parameter model. The characteristics of the frequency-domain impedance and phase waveform at the beginning of the cable are explored, and it is found that they have the characteristics of continuously decreasing with the increase of frequency and periodic changes. However, it is difficult to distinguish the ablation degree and location of the buffer layer only by analyzing the waveform characteristics. Convolutional neural networks can effectively extract data information and make corresponding judgments. In this study, based on the waveform characteristics of the cables, probabilistic clustering processing is conducted on the features, and then the data information is analyzed in combination with the convolutional neural network. The results show that this detection method can achieve an inspection with an accuracy rate of 0.95. 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