Fault diagnosis for VSC-HVDC transmission systems using Kalman Filter-based disturbance observers

Fault diagnosis for VSC-HVDC transmission systems using Kalman Filter-based disturbance observers | 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 Fault diagnosis for VSC-HVDC transmission systems using Kalman Filter-based disturbance observers Gerasimos Rigatos, Zhiwei Gao, Krishna Busawon, Pierluigi Siano, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5168381/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 In this article a Kalman Filter-based disturbance observer is proposed for treating the fault detection and isolation problem of the VSC-HVDC transmission system. By proving differential flatness properties for the state-space model of the VSC-HVDC system which consists of an AC/DC converter-(rectifier), a high-voltage DC transmission line and a DC/AC inverter it is confirmed that this system can be transformed into the input-output linearized form and in the canonical Brunovsky form. The effects of model uncertainty and external perturbations are modeled as additive disturbance inputs. Moreover, by considering the disturbance inputs as additional state variables an extended state space description is obtained which is proven to be observable. For the latter state-space model a Kalman Filter-based disturbance observer is designed which is capable of identifying simultaneously both the non-measurable state variables and the disturbance terms. Next, the residuals of the Kalman Filter undergo statistical signal processing which finally enables fault detection and isolation. The sum of the squares of the residuals' sequence weighted by the inverse of the associated covariance matrix is a stochastic variable or "statistical test" which follows the $\chi^2$ distribution. The confidence intervals of the $\chi^2$ distribution allow to define fault thresholds. By comparing the "statistical test" against the fault thresholds one can conclude with a high level of confidence (of the order of 95\% and beyond) the existence of a failure. The disturbance estimates which are provided by the Kalman Filter-based disturbance observer allow also to perform fault isolation. VSC-HVDC transmission system smart grid multi-variable systems fault detection fault isolation differential flatness properties Kalman Filtering disturbance observers confidence intervals 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5168381","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359981204,"identity":"f2d0a048-a01b-4829-bf77-bdd88aa0040f","order_by":0,"name":"Gerasimos 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"VSC-HVDC transmission system, smart grid, multi-variable systems, fault detection, fault isolation, differential flatness properties, Kalman Filtering, disturbance observers, confidence intervals","lastPublishedDoi":"10.21203/rs.3.rs-5168381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5168381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this article a Kalman Filter-based disturbance observer is proposed for treating the fault detection and isolation problem of the VSC-HVDC transmission system. By proving differential flatness properties for the state-space model of the VSC-HVDC system which consists of an AC/DC converter-(rectifier), a high-voltage DC transmission line and a DC/AC inverter \u0026nbsp;it is confirmed that this system \u0026nbsp;can be transformed into the input-output linearized form and in the canonical Brunovsky form. The effects of model uncertainty and external perturbations are modeled as additive disturbance inputs. Moreover, by considering the disturbance inputs as additional state variables an extended state space description is obtained which is proven to be observable. For the latter state-space model a Kalman Filter-based disturbance observer is designed which is capable of identifying simultaneously both the non-measurable state variables and the disturbance terms. Next, the residuals of the Kalman Filter undergo statistical signal processing which finally enables fault detection and isolation. The sum of the squares of the residuals' sequence weighted by the inverse of the associated covariance matrix is a stochastic variable or \"statistical test\" which follows the $\\chi^2$ distribution. The confidence intervals of the $\\chi^2$ distribution allow to define fault thresholds. By comparing the \"statistical test\" against the fault thresholds one can conclude with a high level of confidence (of the order of 95\\% and beyond) the existence of a failure. The disturbance estimates which are provided by the Kalman Filter-based disturbance observer allow also to perform fault isolation.\u003c/p\u003e","manuscriptTitle":"Fault diagnosis for VSC-HVDC transmission systems using Kalman Filter-based disturbance observers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-01 02:23:05","doi":"10.21203/rs.3.rs-5168381/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"87a33995-71ba-4f63-81d5-2faf22b528a1","owner":[],"postedDate":"October 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-01T02:23:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-01 02:23:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5168381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5168381","identity":"rs-5168381","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}