Research on Detection and Defense Methods for False Data Injection Attacks in Power Systems Based on State-Space Decomposition

Research on Detection and Defense Methods for False Data Injection Attacks in Power Systems Based on State-Space Decomposition | 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 Detection and Defense Methods for False Data Injection Attacks in Power Systems Based on State-Space Decomposition Chao Hong, Zhihong Liang, Yiwei Yang, Pandeng Li, Lin Chen, Leyi Bi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6259513/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract With increasing renewable energy integration, load frequency control (LFC) faces security risks from false data injection attacks (FDIAs). Existing detection methods struggle to distinguish control input attacks from measurement attacks, affecting system stability. This paper develops a state-space model for LFC incorporating renewable energy and energy storage systems (ESS) and analyzes FDIA impacts on system dynamics. A state-space decomposition method is used to decouple attack signals, improving detection accuracy. A sliding mode observer (SMO)-based attack estimation (AE) method is proposed for real-time detection. Additionally, an attack-resilient control (ARC) strategy is designed using H ∞ control theory to enhance robustness. Simulations show that the proposed method reduces AE mean squared error by nearly 30% and improves frequency response stability. These results confirm its effectiveness in detecting FDIAs and enhancing power system security. LFC FDIA state-space decomposition SMO anti-attack control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviews received at journal 30 Mar, 2025 Reviews received at journal 29 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 19 Mar, 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. 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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-6259513","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439397398,"identity":"f9f83b43-8645-4559-a34f-9492c1350d9c","order_by":0,"name":"Chao 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Existing detection methods struggle to distinguish control input attacks from measurement attacks, affecting system stability. This paper develops a state-space model for LFC incorporating renewable energy and energy storage systems (ESS) and analyzes FDIA impacts on system dynamics. A state-space decomposition method is used to decouple attack signals, improving detection accuracy. A sliding mode observer (SMO)-based attack estimation (AE) method is proposed for real-time detection. Additionally, an attack-resilient control (ARC) strategy is designed using \u003cem\u003eH\u003c/em\u003e\u003csub\u003e∞\u003c/sub\u003e control theory to enhance robustness. Simulations show that the proposed method reduces AE mean squared error by nearly 30% and improves frequency response stability. 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