Research on the Construction and Effectiveness of a Computer-Aided Systematic Training Framework for Wind Farm O&M Quality Amidst High Personnel Turnover

Research on the Construction and Effectiveness of a Computer-Aided Systematic Training Framework for Wind Farm O&M Quality Amidst High Personnel Turnover | 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 Research on the Construction and Effectiveness of a Computer-Aided Systematic Training Framework for Wind Farm O&M Quality Amidst High Personnel Turnover Yan Zhang, Jixiang Wang, Wanlu Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9217184/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 Amid the rapid growth of the wind power sector, high turnover of Operations and Maintenance (O&M) personnel poses a critical human factor risk to wind farm reliability. To address this, a computer-aided Systematic Quality Training System (QTS) was developed to ensure skill continuity and quality control. The system integrates intelligent algorithms, digital SOP mapping, and task competency modeling to close skill gaps and reduce inconsistencies. Structured into three functional modules—Quality Awareness, Critical Technical Skills, and Standardized Process Compliance—it utilizes algorithm-based module matching, simulation-driven training, and compliance monitoring. A data-driven, closed-loop mechanism aligns tasks with operator profiles, enabling adaptive path generation and continuous feedback. Deployed across multiple wind farms and over 200 O&M personnel, the system achieved a 32% reduction in operational errors, a marked decrease in defect recurrence, and improved task execution consistency. These results confirm that leveraging computer technologies in structured training enhances operational resilience and stabilizes output under high personnel turnover. Energy Engineering Artificial Intelligence and Machine Learning Industrial Engineering Wind farm operation and maintenance Personnel turnover Quality training system Human factor risk Standardized operations Operational reliability 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. We do this by developing innovative software and high quality services for the global research community. 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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-9217184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611689884,"identity":"74011eda-64cc-4da1-957b-89018491ee0b","order_by":0,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABO0lEQVRIie3PPUsDMRjA8ScE6nLh1oMK9xVSOoggXf0QLncctIvBuh0oNKCki9L1imC/Qosg3cwRqEvw1kId7hCclS4FQY0otfYFOwref7lcyI8nAcjL+4s5AHL6g/jnV3rhDgDG/BeCpwTJVFfNBlpJYJ7gODtVM0N/tnVxkql6CH7LTuKHw37lwN09T6XPkz27acgkvJ4nm/cDqiINfjsKcLmtg+2evqXS749YpBBHZ3q0cC/Ho4oIYN0hLhSJwLQUVUH6esS4IRiJJaT2pMgrsJtEbbwQ0aClzqMh4o51VpJ9M4WbKRAUMBGKuk7hg0jWXU3qyhpAIxoG5SIx76CWuZinA9YzJF76ltrV2DqGst2KszHpH1G3OcDPk7DCLhMVp5NwgXz19r2kcmZfwjq5fK1jeXl5ef+odzhnfIDDCN/vAAAAAElFTkSuQmCC","orcid":"","institution":"Vestas American Wind Technology, Houston, USA, 77002","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":611689885,"identity":"618b8088-158a-4ac3-a6d8-3e333de355c7","order_by":1,"name":"Jixiang Wang","email":"","orcid":"","institution":"Goldwind International Georgia, Tbilisi, Georgia, 0162","correspondingAuthor":false,"prefix":"","firstName":"Jixiang","middleName":"","lastName":"Wang","suffix":""},{"id":611689886,"identity":"fe0a398d-577e-4f1a-9009-eb5a14026e25","order_by":2,"name":"Wanlu Gu","email":"","orcid":"","institution":"eDF power solutions China, Beijing, China, 100020","correspondingAuthor":false,"prefix":"","firstName":"Wanlu","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2026-03-25 02:28:53","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9217184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9217184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566535,"identity":"73dc87df-b716-42ec-bc6d-711b7793f2f7","added_by":"auto","created_at":"2026-03-27 12:56:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1228256,"visible":true,"origin":"","legend":"","description":"","filename":"3YanZhangDMIT2026zz4003.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9217184/v1_covered_33aad307-13dd-438a-b825-03587ff70ae1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eResearch on the Construction and Effectiveness of a Computer-Aided Systematic Training Framework for Wind Farm O\u0026amp;M Quality Amidst High Personnel Turnover\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":"Wind farm operation and maintenance, Personnel turnover, Quality training system, Human factor risk, Standardized operations, Operational reliability","lastPublishedDoi":"10.21203/rs.3.rs-9217184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9217184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmid the rapid growth of the wind power sector, high turnover of Operations and Maintenance (O\u0026amp;M) personnel poses a critical human factor risk to wind farm reliability. 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