{"paper_id":"14ec317d-2581-4fbb-8db1-71d28c237ead","body_text":"Robust Algorithm Development of Frequency Estimation in Smart Grid | 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 Robust Algorithm Development of Frequency Estimation in Smart Grid Yongqian Yu, Yi Yang, Xinyang Wang, Lehao Lv, Yuan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5081483/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Smart grid is an intelligent power generation system in modern electricity networks, with the commonly used model being the three-phase power system. Natural and equipment factors such as lightning, high-power equipment start and stop, switch on and off, and corona discharge of high-voltage transmission lines may produce sudden strong, short duration, high instantaneous power pulse noise in the power system. This paper addresses robust parameter estimation in the presence of impulsive noise. After converting the three-phase waveforms into a pair of orthogonal signals via-transformation, an-norm based robust estimator is developed to accurately find the frequency, phase, and voltage parameters. A new adaptive filtering algorithm, Improved ACLMS (IACLMS), was developed by updating filter coefficients using the-th power of estimation error as cost function. Computer simulations demonstrate that compared with the ACLMS algorithm, the IACLMS algorithm has stronger robustness and faster convergence speed, but slightly lacks precision in frequency estimation. Therefore, based on this method, a nonlinear function relationship between the step size factor and the estimation error was established, and a new variable step size IACLMS (VSS-IACLMS) algorithm was obtained. This method improves the frequency estimation accuracy compared to the IACLMS algorithm, and its mean square error performance can reach the Cramér-Rao lower bound. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Three-phase voltage Impulse noise Improved Augmented Complex Least Mean Square (IACLMS) Variable Step Size Improved Augmented Complex Least Mean Square (VSS-IACLMS) Frequency estimatio Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Feb, 2025 Reviews received at journal 02 Jan, 2025 Reviewers agreed at journal 02 Jan, 2025 Reviews received at journal 03 Nov, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers invited by journal 09 Oct, 2024 Editor assigned by journal 09 Oct, 2024 Editor invited by journal 30 Sep, 2024 Submission checks completed at journal 26 Sep, 2024 First submitted to journal 13 Sep, 2024 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-5081483\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":414538453,\"identity\":\"a594bf65-df29-4934-bcb7-f18df8731100\",\"order_by\":0,\"name\":\"Yongqian Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Science and Technology Beijing\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yongqian\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":414538454,\"identity\":\"2e205449-6c64-42f1-a893-56ab02c7d556\",\"order_by\":1,\"name\":\"Yi 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