A learning-driven algorithm for maintenance team and UAV collaboration in restoring power network

A learning-driven algorithm for maintenance team and UAV collaboration in restoring power 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 Article A learning-driven algorithm for maintenance team and UAV collaboration in restoring power network Tiejun Pan, Leina Zheng, Ying Xu, Xuefeng Zhang, Caiming Zhong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5708499/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Power networks are highly vulnerable to disruptions caused by natural and man-made disasters, necessitating prompt restoration of damaged power supply. This research addresses the challenge of efficiently restoring large-scale power networks, which often involve numerous unknown or uninspected faulty nodes. Leveraging advancements in unmanned aerial vehicles (UAVs) technology, this study facilitates the inspection of these nodes and subsequent manual maintenance. However, coordinating maintenance teams and UAVs is complex due to the intricate network structure and scheduling correlations. We propose a learning-driven (LD) algorithm to enhance human-UAV collaboration for effective power network restoration. The algorithm includes an initialization method to generate promising initial solutions, followed by the use of search operators as basic action elements and a learning engine to guide search directions based on state assessments. Comprehensive experiments validate the algorithm’s effectiveness in improving the restoration process. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Jan, 2025 Reviews received at journal 18 Jan, 2025 Reviews received at journal 12 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviewers agreed at journal 10 Jan, 2025 Reviewers invited by journal 10 Jan, 2025 Editor assigned by journal 10 Jan, 2025 Editor invited by journal 09 Jan, 2025 Submission checks completed at journal 09 Jan, 2025 First submitted to journal 24 Dec, 2024 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-5708499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":401038419,"identity":"ffe38826-bb76-4f34-9f6d-7a3d4f92e07e","order_by":0,"name":"Tiejun Pan","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Tiejun","middleName":"","lastName":"Pan","suffix":""},{"id":401038420,"identity":"669f0f21-64e6-49bd-9945-41ad2e0d6f33","order_by":1,"name":"Leina Zheng","email":"","orcid":"","institution":"Zhejiang Wanli University","correspondingAuthor":false,"prefix":"","firstName":"Leina","middleName":"","lastName":"Zheng","suffix":""},{"id":401038421,"identity":"c2258554-9fec-4d00-beb8-506546cfc50d","order_by":2,"name":"Ying Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYJCCAwwMNnZs7M0HmBkY5EACBsRoSUvm5zmWANRiTJwWIDjMOHNGjgFxWgxu5BgeLviVxmxwIOfb48I2AzkG9uZtEgw1d3BqkQQafnhmnw2fwYGz241nthkYM/AcK5NgOPYMpxZ+CaAW3h6gLQd7t0nztv1JbJDIMZNgbDiMUwsbRMthxg2HeZ4BtRjUN8i/wa8FbAvPD6D323jYQFoSGCR48GuR7HlWcJi3ARTIbGbSM84ZGLbxpBVbJBzDrcXgePLmzzx/gFEp//iZdEGZgTw/++GNNz7U4NbCwMBhwMDYhuw7EJGARwMDA/sDBoY/eFWMglEwCkbBSAcAjStS2eJsT4sAAAAASUVORK5CYII=","orcid":"","institution":"Ningbo University of Finance \u0026 Economics","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Xu","suffix":""},{"id":401038422,"identity":"bef1432c-10f5-462c-818c-bbe0b16c4495","order_by":3,"name":"Xuefeng Zhang","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Zhang","suffix":""},{"id":401038423,"identity":"5bf0b2bc-4709-4618-a46a-dffbe92485b1","order_by":4,"name":"Caiming Zhong","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Caiming","middleName":"","lastName":"Zhong","suffix":""},{"id":401038424,"identity":"28818043-bffd-4eb3-b49c-f80c2a311ad5","order_by":5,"name":"Zhang Wang","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-12-25 01:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5708499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5708499/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-06512-w","type":"published","date":"2025-07-02T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86180099,"identity":"33c4c0a2-3313-4d0f-8c25-e40925828ad8","added_by":"auto","created_at":"2025-07-07 16:21:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":832078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5708499/v1_covered_13b6719e-45e3-4f9c-8ceb-5cec2aaf3aae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A learning-driven algorithm for maintenance team and UAV collaboration in restoring power network","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5708499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5708499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Power networks are highly vulnerable to disruptions caused by natural and man-made disasters, necessitating prompt restoration of damaged power supply. 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