Improved 3DCNN-based tripping prediction method due to lightning strikes on transmission lines under unbalanced samples

Improved 3DCNN-based tripping prediction method due to lightning strikes on transmission lines under unbalanced samples | 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 Improved 3DCNN-based tripping prediction method due to lightning strikes on transmission lines under unbalanced samples Yongli Liu, Xiaowei Yang, Shuaibin Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6175767/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Electrical Engineering → Version 1 posted 10 You are reading this latest preprint version Abstract Lightning strike-induced transmission line tripping is an important reason affecting the safe operation of power grids. Timely and accurate prediction of transmission line tripping can provide a basis for power companies to adjust their operation modes beforehand and reduce economic losses. The traditional prediction model of line tripping due to lightning strikes predicts whether tripping will occur in the next time period based on the monitoring data of the lightning locating system (LLS) in one time period, which makes it difficult to learn the evolution pattern of lightning activity in space and time. Therefore, this paper proposes an improved 3-dimensional convolutional neural network (3DCNN) based tripping prediction method for transmission lines. Firstly, based on the monitoring data from the LLS, a high-dimensional input matrix construction method considering the spatial-temporal characteristics of lightning activities is proposed. Secondly, based on the 3DCNN, self-attention mechanism and hierarchical classifier, the improved 3DCNN is proposed to learn the spatial-temporal law of lightning activities to realize the prediction of whether the transmission line is tripped or not, as well as the location of lightning strikes on lines. Subsequently, considering the imbalance of the samples, a focal loss function is introduced to guide the model training, which improves the accuracy of the model. Finally, the measured data of a local power grid in southern China is used as an example to verify the effectiveness and reliability of the method in this paper. lightning location system 3DCNN transmission line tripping sample imbalance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Electrical Engineering → Version 1 posted Editorial decision: Revision requested 29 May, 2025 Reviews received at journal 19 May, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 07 Mar, 2025 Submission checks completed at journal 07 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. 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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-6175767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425568068,"identity":"dec209d3-90bf-4552-b942-dfdacdd5ab08","order_by":0,"name":"Yongli Liu","email":"","orcid":"","institution":"Shenzhen Power Supply Co, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yongli","middleName":"","lastName":"Liu","suffix":""},{"id":425568069,"identity":"7a014050-ddb5-4550-8319-7d7c984d9de8","order_by":1,"name":"Xiaowei Yang","email":"","orcid":"","institution":"Shenzhen Power Supply Co, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Yang","suffix":""},{"id":425568070,"identity":"d5c1bc31-f101-4108-9f54-46430db34267","order_by":2,"name":"Shuaibin Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCSBObACRPAwHPhjY2JGm5eCMgrRk4rQwNjCAtTDzfDgEYeMD8rN7DD883GGRx8B/9uBhG4MDzAzsh49uwKfF4M4ZY4nEMxLFDBJ5CYdzDO7wMfCkpd3Aq0Uix0AisU0C6B0eA6CWZ8wMEjxmeLXIz8gx/gHWwn/G4LCFwWHGBkJaGG7kmEFsYcgxOMxAjBaDG2llFkC/AB2WY3CwxyAtmY2QX+RnJG+++XNHHchhxh9+/LGx42c/fAy/w2DA/gCUwUaU8lEwCkbBKBgFeAEA5BtKx2PfdVEAAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen Power Supply Co, Ltd","correspondingAuthor":true,"prefix":"","firstName":"Shuaibin","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-03-07 07:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6175767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6175767/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00202-025-03441-9","type":"published","date":"2026-01-19T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101152126,"identity":"eb81a323-4d1b-42a3-93fd-b0c28b2a2449","added_by":"auto","created_at":"2026-01-26 16:10:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":505218,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6175767/v1_covered_b9b0df6a-1fdb-42cd-805f-fa1ba561025f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved 3DCNN-based tripping prediction method due to lightning strikes on transmission lines under unbalanced samples","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"elen","sideBox":"Learn more about [Electrical Engineering](http://link.springer.com/journal/202)","snPcode":"202","submissionUrl":"https://submission.nature.com/new-submission/202/3","title":"Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"lightning location system, 3DCNN, transmission line tripping, sample imbalance","lastPublishedDoi":"10.21203/rs.3.rs-6175767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6175767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Lightning strike-induced transmission line tripping is an important reason affecting the safe operation of power grids. Timely and accurate prediction of transmission line tripping can provide a basis for power companies to adjust their operation modes beforehand and reduce economic losses. The traditional prediction model of line tripping due to lightning strikes predicts whether tripping will occur in the next time period based on the monitoring data of the lightning locating system (LLS) in one time period, which makes it difficult to learn the evolution pattern of lightning activity in space and time. Therefore, this paper proposes an improved 3-dimensional convolutional neural network (3DCNN) based tripping prediction method for transmission lines. Firstly, based on the monitoring data from the LLS, a high-dimensional input matrix construction method considering the spatial-temporal characteristics of lightning activities is proposed. Secondly, based on the 3DCNN, self-attention mechanism and hierarchical classifier, the improved 3DCNN is proposed to learn the spatial-temporal law of lightning activities to realize the prediction of whether the transmission line is tripped or not, as well as the location of lightning strikes on lines. Subsequently, considering the imbalance of the samples, a focal loss function is introduced to guide the model training, which improves the accuracy of the model. 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