Research on Power Signal Processing and Feature Extraction Algorithm Based on Time-Frequency Analysis

Research on Power Signal Processing and Feature Extraction Algorithm Based on Time-Frequency Analysis | 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 Power Signal Processing and Feature Extraction Algorithm Based on Time-Frequency Analysis Guanghua Yang, Rui Li, Xiangyu Lu, Yuexiao Liu, Na Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6703858/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 Power signal processing is a specialized domain within signal processing that focuses on the analysis, interpretation, and manipulation of signals in electrical power systems. In modern smart grids, Power Quality Disturbances (PQDs) can result in considerable operational disruptions and financial losses for energy stakeholders. Accurate and timely identification of these disturbances is critical to maintaining grid reliability, efficiency, and energy stability. To overcome these challenges, the research proposes a comprehensive framework for PQD identification by leveraging advanced power signal processing techniques and time-frequency-based feature extraction. A Short-Time Fourier Transform fused Efficient Natural Gradient Boosting (STFT-ENGB) model is introduced for robust recognition of power quality disturbances with energy grid applications. To improve computational efficiency and decrease redundant data collection, a signal-piloted gain device is employed. This device continuously monitors power signals and initiates data acquisition only when abnormalities or potential disturbances are detected. The Z-score normalization is a preprocessing technique for reducing noise. The STFT is utilized to extract discriminative, time-localized features from the power signals, effectively characterizing voltage fluctuations and transient energy anomalies. These extracted features are subsequently used to train and evaluate the ENGB classifier. The proposed STFT-ENGB approach achieves high accuracy (98.75%). Experimental results demonstrate that the proposed framework achieves high classification accuracy while significantly reducing data volume and computational load. The reduction in processing overhead and latency underscores the system's suitability for real-time smart grid applications. The proposed approach offers a promising solution for real-time power signal monitoring in smart grid environments, facilitating intelligent fault diagnosis and improving the overall resilience and responsiveness of modern electrical infrastructure. Power Signal Processing Power Quality Disturbances (PQDs) Smart Grid Signal-Piloted Short-Time Fourier Transform fused Efficient Natural Gradient Boosting (STFT-ENGB) Full Text Additional Declarations No competing interests reported. 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-6703858","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472635336,"identity":"e379be28-6507-4569-9de2-8ec0fc14a0f8","order_by":0,"name":"Guanghua Yang","email":"data:image/png;base64,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","orcid":"","institution":"State Grid Beijing Electric Power Company","correspondingAuthor":true,"prefix":"","firstName":"Guanghua","middleName":"","lastName":"Yang","suffix":""},{"id":472635337,"identity":"9179618d-5d6f-43c9-81e6-e2609e40c770","order_by":1,"name":"Rui Li","email":"","orcid":"","institution":"State Grid Beijing Electric Power Company","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":472635338,"identity":"fba050a8-5dc0-49ed-a988-d57276073ab6","order_by":2,"name":"Xiangyu Lu","email":"","orcid":"","institution":"State Grid Beijing Electric Power Company","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Lu","suffix":""},{"id":472635339,"identity":"81f448cd-cdc3-47d5-9c54-b5a0542aa001","order_by":3,"name":"Yuexiao Liu","email":"","orcid":"","institution":"State Grid Beijing Electric Power Company","correspondingAuthor":false,"prefix":"","firstName":"Yuexiao","middleName":"","lastName":"Liu","suffix":""},{"id":472635340,"identity":"66c12a94-bf3c-4210-bf36-bd6d297d5a98","order_by":4,"name":"Na Li","email":"","orcid":"","institution":"State Grid Beijing Electric Power Company","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-05-20 05:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6703858/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6703858/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87644631,"identity":"1200a586-ec1c-492d-be46-da9c32f83de1","added_by":"auto","created_at":"2025-07-26 17:01:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877541,"visible":true,"origin":"","legend":"","description":"","filename":"EI4131.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703858/v1_covered_f8cf5b24-a5eb-472b-a956-a1d785e6a992.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Power Signal Processing and Feature Extraction Algorithm Based on Time-Frequency Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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},"keywords":"Power Signal Processing, Power Quality Disturbances (PQDs), Smart Grid, Signal-Piloted, Short-Time Fourier Transform fused Efficient Natural Gradient Boosting (STFT-ENGB)","lastPublishedDoi":"10.21203/rs.3.rs-6703858/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6703858/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePower signal processing is a specialized domain within signal processing that focuses on the analysis, interpretation, and manipulation of signals in electrical power systems. In modern smart grids, Power Quality Disturbances (PQDs) can result in considerable operational disruptions and financial losses for energy stakeholders. Accurate and timely identification of these disturbances is critical to maintaining grid reliability, efficiency, and energy stability. To overcome these challenges, the research proposes a comprehensive framework for PQD identification by leveraging advanced power signal processing techniques and time-frequency-based feature extraction. A Short-Time Fourier Transform fused Efficient Natural Gradient Boosting (STFT-ENGB) model is introduced for robust recognition of power quality disturbances with energy grid applications. To improve computational efficiency and decrease redundant data collection, a signal-piloted gain device is employed. This device continuously monitors power signals and initiates data acquisition only when abnormalities or potential disturbances are detected. 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