Intelligent Particle Filtering State Observer for Stability Assessment in Solar-Wind Penetrated Microgrids

Intelligent Particle Filtering State Observer for Stability Assessment in Solar-Wind Penetrated Microgrids | 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 Article Intelligent Particle Filtering State Observer for Stability Assessment in Solar-Wind Penetrated Microgrids ABDULELAH ALHARBI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7441835/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This paper presents an advanced Intelligent Particle Filtering State Observer (PFSO) for real-time voltage and frequency stability assessment in microgrids integrated with high-penetration solar and wind energy sources. The proposed method leverages the robustness of PFSO to address the nonlinear, stochastic, and dynamic behaviors inherent in renewable-based distributed generation systems. A comprehensive state-space model of the microgrid is developed, and the PFSO is employed to estimate unmeasurable or noisy system states in the presence of process and measurement uncertainties. The proposed method was validated in MATLAB/Simulink across three scenarios: normal operation, sudden power mismatch, and periodic load disturbance. Quantitative results demonstrate that the PFSO maintains high estimation accuracy, with Root Mean Square Error (RMSE) values consistently below 0.0095 per unit (p.u.) and Mean Absolute Error (MAE) under 0.0073 p.u. for both voltage and frequency states. The maximum estimation error remained below 0.020 p.u., confirming strong robustness under transient conditions. Furthermore, a binary classification analysis of system stability, using a 0.95 p.u. threshold achieved 97.4% accuracy, 95.9% precision, and an F1-score of 96.5% across all cases. The findings validate the effectiveness of the proposed PFSO as a reliable tool for dynamic state estimation and early instability detection in smart microgrid environments. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Dynamic state estimation Frequency stability Microgrid monitoring Particle filter Renewable energy integration Smart grids Solar energy Stability assessment Voltage estimation Wind energy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 27 Aug, 2025 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-7441835","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":516464783,"identity":"36ad472f-ba4f-4c5b-87c7-a7b655129fd0","order_by":0,"name":"ABDULELAH ALHARBI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3RMQrCMBiG4b8EqkPVtaVFr/CLg4viVVq8gCCIm0ogLh6got5Bb6BkcBHnig52caqgm4ODiYhuqaNg3iGUwMNHCYBO94OZGXm2lgC2MTg+r5YppEDkiU9C8Svi0DcB0/6KILcQbngoVSeUdc8MivnIJ5dbCjFGeCpPDyu2mzGoOJFvOqO0FQu5EdoB2+cYBHNBwFKQhly5I29I0hakJwi53lNWiFgJJCGC+Bj54KpWkJst7iFvht6KOtOtXR5vYuZ6KrKmizjp8nroDuNL0qmV8usmvyYKApCVj/LKAvk0Rl8JADLHz7fqF3Q6ne5/ewA1sFB/dkg+eAAAAABJRU5ErkJggg==","orcid":"","institution":"Qassim University","correspondingAuthor":true,"prefix":"","firstName":"ABDULELAH","middleName":"","lastName":"ALHARBI","suffix":""}],"badges":[],"createdAt":"2025-08-23 14:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7441835/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7441835/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27918-6","type":"published","date":"2025-12-23T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172304,"identity":"d52c4580-5fe6-4fe9-8698-b2340431e2e5","added_by":"auto","created_at":"2025-12-29 16:07:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981556,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7441835/v1_covered_6dc20d32-bd0d-4256-a508-0b6d865ec142.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent Particle Filtering State Observer for Stability Assessment in Solar-Wind Penetrated Microgrids","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dynamic state estimation, Frequency stability, Microgrid monitoring, Particle filter, Renewable energy integration, Smart grids, Solar energy, Stability assessment, Voltage estimation, Wind energy","lastPublishedDoi":"10.21203/rs.3.rs-7441835/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7441835/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents an advanced Intelligent Particle Filtering State Observer (PFSO) for real-time voltage and frequency stability assessment in microgrids integrated with high-penetration solar and wind energy sources. 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