Simultaneous Faults and Cyber-Attacks Diagnosis in Wind Turbines; An LMI Approach using Memory-based Dynamic Residual Field

Simultaneous Faults and Cyber-Attacks Diagnosis in Wind Turbines; An LMI Approach using Memory-based Dynamic Residual Field | 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 Simultaneous Faults and Cyber-Attacks Diagnosis in Wind Turbines; An LMI Approach using Memory-based Dynamic Residual Field Mehdi Shakeri, Mehrdad Babazadeh, Mahdi Khodabandeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8811239/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract This paper presents a unified diagnostic framework for detecting actuator faults, sensor faults, and cyber-attacks in wind turbines operating under uncertainty, nonlinear perturbation and external disturbance. The core innovation is the Memory-based Dynamic Residual Field (MDRF), a dynamic structure that tracks both estimation errors and their temporal evolution, ensuring high sensitivity to anomalies with robust disturbance rejection. Using a state observer for augmented system and minimizing \(\:{H}_{\infty\:}\) gain of disturbances via Linear Matrix Inequality (LMI), an analytical thresholds that guarantee the separation of normal operations from cyber-attacks was derived. Crucially, a cross-correlation logic distinguishes legitimate set-point changes from malicious intrusions. Simulations on a benchmark \(\:4.8MW\) wind turbine confirm accurate, real-time isolation of all scenarios. Notably, a comprehensive comparative study demonstrates that the proposed framework outperforms standard Unknown Input Observers (UIO) in isolating cyber-attacks from legitimate command changes. Also, this model-based approach matches the detection accuracy of Neural Networks while reducing computational load by over \(\:90\%\) , proving its suitability for resource-constrained industrial controllers. Cyber-attack LMI Model-based Observer-based Fault Detection Residual Generation Wind Turbine Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 28 Feb, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 06 Feb, 2026 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-8811239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590452369,"identity":"f8981c00-7413-47b0-a3ed-45dd6e3bfdaa","order_by":0,"name":"Mehdi Shakeri","email":"","orcid":"","institution":"University of Zanjan, University Blvd","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Shakeri","suffix":""},{"id":590452371,"identity":"17ed9a90-6d13-4bbc-b890-7314b9e60b8e","order_by":1,"name":"Mehrdad Babazadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYFACHgZmhgoYx4BoLWdI1sLYRoqzdNt7D34unFeXZ3CA+eEHhoJ7hLWYnTmXLD1zG1uxwQE2YwkGg2IitNzIMZDm3caTuOEAgxnQLwlEaLn/xvg37xwJoBb2b0RqucFjJs3bYADUwkOsLWfy0qxnHEtInHmYp1gigSgtx88evl1QU5fYd7x944cPf4jQggDMQEyShlEwCkbBKBgFuAEANvA1Czw6yTIAAAAASUVORK5CYII=","orcid":"","institution":"University of Zanjan, University Blvd","correspondingAuthor":true,"prefix":"","firstName":"Mehrdad","middleName":"","lastName":"Babazadeh","suffix":""},{"id":590452372,"identity":"10fb5cf9-2a4f-4de8-81cf-72224660e1b8","order_by":2,"name":"Mahdi Khodabandeh","email":"","orcid":"","institution":"University of Zanjan, University Blvd","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"","lastName":"Khodabandeh","suffix":""}],"badges":[],"createdAt":"2026-02-06 23:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8811239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8811239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102747269,"identity":"b2025955-0023-417a-9add-e5c04a386ea4","added_by":"auto","created_at":"2026-02-16 09:04:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026429,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8811239/v1_covered_f0f3df8f-b712-4c9e-82e3-160c76e8834c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simultaneous Faults and Cyber-Attacks Diagnosis in Wind Turbines; An LMI Approach using Memory-based Dynamic Residual Field","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"iranian-journal-of-science-and-technology-transactions-of-electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iste","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Electrical Engineering](https://link.springer.com/journal/40998)","snPcode":"40998","submissionUrl":"https://submission.springernature.com/new-submission/40998/3","title":"Iranian Journal of Science and Technology, Transactions of Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cyber-attack, LMI, Model-based, Observer-based Fault Detection, Residual Generation, Wind Turbine","lastPublishedDoi":"10.21203/rs.3.rs-8811239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8811239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a unified diagnostic framework for detecting actuator faults, sensor faults, and cyber-attacks in wind turbines operating under uncertainty, nonlinear perturbation and external disturbance. The core innovation is the Memory-based Dynamic Residual Field (MDRF), a dynamic structure that tracks both estimation errors and their temporal evolution, ensuring high sensitivity to anomalies with robust disturbance rejection. Using a state observer for augmented system and minimizing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{\\infty\\:}\\)\u003c/span\u003e\u003c/span\u003e gain of disturbances via Linear Matrix Inequality (LMI), an analytical thresholds that guarantee the separation of normal operations from cyber-attacks was derived. Crucially, a cross-correlation logic distinguishes legitimate set-point changes from malicious intrusions. Simulations on a benchmark \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:4.8MW\\)\u003c/span\u003e\u003c/span\u003e wind turbine confirm accurate, real-time isolation of all scenarios. 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