Using data mining techniques to improve the efficiency of power system measurement data analysis

Using data mining techniques to improve the efficiency of power system measurement data 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 Using data mining techniques to improve the efficiency of power system measurement data analysis Lingjie Xu, Xiao Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6704408/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 Efficiently processing power system measurement data is critical for guaranteeing dependable and cost-effective operation in modern electrical grids. However, the massive amounts of data created by power systems can exceed typical analysis methods, resulting in delayed responses and ineffective management. This research seeks to improve the efficiency of power system data analysis by employing data mining techniques to identify trends and discover anomalies in measurement data. This research offers a novel method, Dwarf Mongoose Optimized Multi-Layer Perceptron with Isolation Forest (DMO-MLP-IsoForest) that combines an MLP for classification tasks with an IsoForest for anomaly detection. DMO is used to optimize the hyper parameters of models, assuring great accuracy and efficiency in identifying and classifying anomalies. The dataset is made up of real-time voltage, current, and power data collected from multiple sensors across the grid, which is cleaned and normalized to ensure consistency and accuracy. K-means clustering is used to group similar data points during feature extraction, which aids in the discovery of underlying patterns in the power system's behaviour. Results, accuracy (0.985), precision (0.995), recall (0.974), f1-measure (0.985) and specificity (0.987), show that the proposed DMO-MLP-IsoForest methods significantly increase the speed and accuracy of power system data processing when compared to standard methodologies. Finally, data mining provides an effective technique to optimize the analysis of power system measurement data, hence improving the grid's operating efficiency and dependability. Power System Data Analysis Anomaly Detection K-means clustering Dwarf Mongoose Optimized Multi-Layer Perceptron’s with Isolation Forest (DMO-MLP-IsoForest) Efficiency Consistency Voltage Sensors and Data mining 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-6704408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472048601,"identity":"405d6cdb-291a-4244-9a8e-237bb93009a1","order_by":0,"name":"Lingjie Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie2QsWrDMBBALwjiRWpXhVL/QeGKwGQw7q9YCNS1Y4YOhkCzmHaNofQbMnmtjMCT/6HK1tGhS4dCq0LGOPFYqN5wx3H3OO4AAoE/CBIfOIIsouL2M1/4KgKYFKMUahzvOwBKTin7LIBLN6sexigRs+/zuzRGo3LBXlL5SkizXUMWXw14SM70nKOmaNpcsVrLkkzV9QaUSMyQQhPkaCk2ZW5Zbb1Ck5kDI+vjyneM9ny3ZM/jFOE4GoEtqElV7JXNiS3+yUpWHWjoWy1K629Z4/AtN0+d+OBfmXx8cxry+/RytVo223KRxUOKZ3rBD2wfHP+F7Pqj/UAgEPj3/ACupVprPdAZzAAAAABJRU5ErkJggg==","orcid":"","institution":"Marketing and Service Center of State Grid Zhejiang Electric Power Co., Ltd.，Hangzhou","correspondingAuthor":true,"prefix":"","firstName":"Lingjie","middleName":"","lastName":"Xu","suffix":""},{"id":472048604,"identity":"b1717c16-5a62-4ab3-9aab-f4cf75ad29aa","order_by":1,"name":"Xiao Chen","email":"","orcid":"","institution":"Marketing and Service Center of State Grid Zhejiang Electric Power Co., Ltd.，Hangzhou","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-05-20 06:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6704408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6704408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100951997,"identity":"d5ca3537-4c37-4b98-bee7-ce9407ad86c3","added_by":"auto","created_at":"2026-01-23 07:11:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155920,"visible":true,"origin":"","legend":"","description":"","filename":"EI412.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6704408/v1_covered_13bc056c-e061-414c-8931-0d125767d8e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using data mining techniques to improve the efficiency of power system measurement data 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 System, Data Analysis, Anomaly Detection, K-means clustering, Dwarf Mongoose Optimized Multi-Layer Perceptron’s with Isolation Forest (DMO-MLP-IsoForest), Efficiency, Consistency, Voltage, Sensors and Data mining","lastPublishedDoi":"10.21203/rs.3.rs-6704408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6704408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEfficiently processing power system measurement data is critical for guaranteeing dependable and cost-effective operation in modern electrical grids. 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