Solar Farms and Power Line Inspection using Unmanned Aerial Vehicles

Solar Farms and Power Line Inspection using Unmanned Aerial Vehicles | 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 Systematic Review Solar Farms and Power Line Inspection using Unmanned Aerial Vehicles Muyombano Happy Axel, Kagwesage Anne Marie, Muhoza Ntege Grace, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6828810/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 increasing demand for reliable and sustainable energy necessitates efficient monitoring and maintenance of power transmission lines and solar farms. Traditional inspection methods are labor-intensive, timeconsuming, and often pose safety risks to personnel. This paper explores the use of Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors and artificial intelligence (AI) for automated inspection and fault detection in power infrastructure. The proposed system integrates high-resolution imaging, thermal sensors, and LiDAR to assess structural integrity, detect anomalies, and enhance predictive maintenance. AI-based computer vision algorithms process collected data to identify defects such as conductor sag, insulator damage, panel degradation, and vegetation encroachment. Additionally, a cloud-based analytics platform enables real-time data transmission and decision support. The study evaluates the efficiency, accuracy, and cost-effectiveness of UAV-based inspections compared to conventional methods. Experimental results demonstrate significant improvements in inspection speed, operational safety, and fault diagnosis accuracy. This research contributes to the optimization of energy infrastructure monitoring, supporting the transition to smart and resilient power grids Energy Engineering Artificial Intelligence and Machine Learning Unmanned Aerial Vehicles Power Line Inspection Solar Farm Monitoring AI-based Preventive Maintenance Full Text Additional Declarations The authors declare no competing interests. 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. 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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-6828810","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":466992545,"identity":"d0b18574-a138-4438-a91b-7f9dbb519e98","order_by":0,"name":"Muyombano Happy Axel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYLACxgYgIQFm2oC4jQdI0ZIG5pKk5TCYxKtFd0aO4aObO+zy+KWbnz0ubDtvt7b9MNCWGptoXFrMbuQYG+eeSS6WnHPM3Hhm2+3kbWcSgVqOpeU24NZiJp3bxpy44UaCmTQvUIvZAaAWxobDhLTUA7WkfwNqOZdsdv4hUVoOA7XkgGw5YGd2g5AtZ54VA/1yPHHmjJxy4xnnkhPMbgBtScDnl+PJGx/n7qhO7JdI3/a4oMzO3ux8+sMHH2pscGphYOAwgLHYmBnZGBLBKhNwKgcB9gcILQx/GOzxKh4Fo2AUjIIRCQDlg2jnuHO0SAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0000-0897-4932","institution":"University of Rwanda","correspondingAuthor":true,"prefix":"","firstName":"Muyombano","middleName":"Happy","lastName":"Axel","suffix":""},{"id":466992546,"identity":"7bcfa96b-4b2b-4a4c-b33e-58a1ff4387bc","order_by":1,"name":"Kagwesage Anne Marie","email":"","orcid":"","institution":"University of Rwanda","correspondingAuthor":false,"prefix":"","firstName":"Kagwesage","middleName":"Anne","lastName":"Marie","suffix":""},{"id":466992547,"identity":"0906ea3c-9ced-4310-b21f-78d329d3d181","order_by":2,"name":"Muhoza Ntege Grace","email":"","orcid":"","institution":"Daystar University","correspondingAuthor":false,"prefix":"","firstName":"Muhoza","middleName":"Ntege","lastName":"Grace","suffix":""},{"id":466992548,"identity":"9ebe5465-7d08-4c05-800e-f43b9dfdaaa9","order_by":3,"name":"Nkusi Geoffrey","email":"","orcid":"","institution":"Rwanda Energy Group","correspondingAuthor":false,"prefix":"","firstName":"Nkusi","middleName":"","lastName":"Geoffrey","suffix":""}],"badges":[],"createdAt":"2025-06-05 11:40:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6828810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6828810/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84041262,"identity":"2c4c05c6-6fb7-4562-8cae-f722245316e5","added_by":"auto","created_at":"2025-06-06 06:00:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":544709,"visible":true,"origin":"","legend":"","description":"","filename":"SolarFarmsandPowerLineInspectionusingUnmannedAerialVehicles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6828810/v1_covered_f0133175-5b23-4b57-bacb-fff69ebff22f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSolar Farms and Power Line Inspection using Unmanned Aerial Vehicles\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Unmanned Aerial Vehicles, Power Line Inspection, Solar Farm Monitoring, AI-based, Preventive Maintenance","lastPublishedDoi":"10.21203/rs.3.rs-6828810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6828810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing demand for reliable and sustainable energy necessitates efficient monitoring and maintenance of power transmission lines and solar farms. 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