Grid-Forming VSG Control Strategy with Adaptive Virtual Inertia and Damping Coefficient

Grid-Forming VSG Control Strategy with Adaptive Virtual Inertia and Damping Coefficient | 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 Grid-Forming VSG Control Strategy with Adaptive Virtual Inertia and Damping Coefficient Xiping Ma, Jixiang Zhao, Lizhen Wu, Heng Yang, Yunpeng Bao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8863776/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The control strategy of virtual synchronous generator (VSG) provides inertia and damping for grid-forming converters during grid-connected operation, and increases the support ability of frequency and voltage of the system. However, after introducing virtual inertia, grid-connected converters are prone to active-power oscillations and power overshoot during disturbances, while the inertia and damping can also slow down the system’s response. To address this issue, this paper first builds the model of the grid-forming VSG. Then, the Transformer neural network is constructed to perform online adaptive tuning of the VSG’s virtual inertia and damping coefficient, and the adjusted parameters are applied to the grid-forming VSG controller. Finally, the dynamic responses of the traditional control strategy and the proposed control strategy are compared by simulation. Simulation results show that the proposed control strategy can significantly reduce the oscillation and overshoot of active power and frequency when the system is disturbed, while maintaining good dynamic response, the effectiveness of the proposed control strategy is thus verified. Grid-Forming Converter Virtual Synchronous Generator (VSG) Transformer Neural Network Virtual Inertia Damping Coefficient Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 25 Mar, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 12 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-8863776","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612629009,"identity":"27e71ffe-150b-4f44-b117-92d6b3892794","order_by":0,"name":"Xiping Ma","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiping","middleName":"","lastName":"Ma","suffix":""},{"id":612629010,"identity":"b671c717-bf92-430c-848e-a5f851612ee2","order_by":1,"name":"Jixiang Zhao","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jixiang","middleName":"","lastName":"Zhao","suffix":""},{"id":612629011,"identity":"beca8cb2-8884-43d8-a57d-e77047fc5ec3","order_by":2,"name":"Lizhen Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYFAC5gaGBwwMcmzs7QeI1cLYwJDAYGDMx3MmgTQtifMkHAyI02BwI7H5Q2Lbn/Q2CaDOHxXbiNLSYJDYZpDbJt14gLHnzG3CWsyAWhLAWmQOJDAzthGp5QBQSzqbRIIB0VoaG4BaEojXYn/mYTNDwjljwzZgIB8kyi+S7cmHP3wok5OXb28/+OBHBRFaUMABEtWPglEwCkbBKMAFAGAOPg5JPIF5AAAAAElFTkSuQmCC","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Lizhen","middleName":"","lastName":"Wu","suffix":""},{"id":612629012,"identity":"8638607c-c452-4d36-944e-f07675aecbda","order_by":3,"name":"Heng Yang","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Yang","suffix":""},{"id":612629013,"identity":"dd3d796c-4c05-490b-a376-6723805e2246","order_by":4,"name":"Yunpeng Bao","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunpeng","middleName":"","lastName":"Bao","suffix":""}],"badges":[],"createdAt":"2026-02-12 15:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8863776/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8863776/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105533025,"identity":"d9fdc967-6e2e-437b-bdf2-78d463cc5bec","added_by":"auto","created_at":"2026-03-27 06:27:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":738834,"visible":true,"origin":"","legend":"","description":"","filename":"GridFormingVSGControlStrategywithAdaptiveVirtualInertiaandDampingCoefficient.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8863776/v1_covered_fa485c19-e8f1-4a9d-a6b8-bf9fa2e0182f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Grid-Forming VSG Control Strategy with Adaptive Virtual Inertia and Damping Coefficient","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":"electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"elen","sideBox":"Learn more about [Electrical Engineering](http://link.springer.com/journal/202)","snPcode":"202","submissionUrl":"https://submission.nature.com/new-submission/202/3","title":"Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Grid-Forming Converter, Virtual Synchronous Generator (VSG), Transformer Neural Network, Virtual Inertia, Damping Coefficient","lastPublishedDoi":"10.21203/rs.3.rs-8863776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8863776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe control strategy of virtual synchronous generator (VSG) provides inertia and damping for grid-forming converters during grid-connected operation, and increases the support ability of frequency and voltage of the system. However, after introducing virtual inertia, grid-connected converters are prone to active-power oscillations and power overshoot during disturbances, while the inertia and damping can also slow down the system\u0026rsquo;s response. To address this issue, this paper first builds the model of the grid-forming VSG. Then, the Transformer neural network is constructed to perform online adaptive tuning of the VSG\u0026rsquo;s virtual inertia and damping coefficient, and the adjusted parameters are applied to the grid-forming VSG controller. Finally, the dynamic responses of the traditional control strategy and the proposed control strategy are compared by simulation. 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