Research on the Prediction Method of MOV Deterioration State Based on Principal Component Analysis and Grid Search- Optimized Support Vector Machine Regression Algorithm

Research on the Prediction Method of MOV Deterioration State Based on Principal Component Analysis and Grid Search- Optimized Support Vector Machine Regression Algorithm | 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 the Prediction Method of MOV Deterioration State Based on Principal Component Analysis and Grid Search- Optimized Support Vector Machine Regression Algorithm Zhiheng Zhu, Zhengwang Xu, Runyang Xiao, Zhou Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4121035/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 Background: Accurate diagnosis of faults in metal oxide varistors (MOV) is crucial for the safe operation of power systems, and the deterioration of MOV under continuous pulse impacts can be more severe. To effectively improve the fault diagnosis rate, this paper proposes a fault diagnosis algorithm based on Principal Component Analysis (PCA) and Grid Search-optimized Support Vector Regression (GS-SVR).Objective, The objective of this study is to propose an effective fault diagnosis algorithm that accurately predicts the fault state of MOV under single and continuous pulse impacts, while reducing the correlation between indication indicators through dimensionality reduction. Method: The proposed experiment involves conducting a comparative test on MOV with different time intervals between impacts, on the order of 10 seconds. The data collected from this experiment, with a time resolution of 10 seconds, will be subjected to dimensionality reduction using PCA to reduce the correlation between the original indicators. Finally, the GS-SVR model will be employed to analyze and predict the effects of single and continuous pulse impacts on MOV. Results: Experimental results demonstrate that the GS-SVR model achieves a mean square error of less than 0.00057 in predicting single pulse impacts and still exhibits certain effectiveness for irregular pulse impacts, such as continuous pulses. Conclusion : The proposed fault diagnosis algorithm based on PCA and GS-SVR can effectively improve the fault diagnosis rate of MOV, and accurately predict the fault state of MOV under single and continuous impulse shock. This is of great significance to the safe operation of power system. Metal oxide varistor Principal Component Analysis Grid Search Support Vector Machine Continuous pulse impact prediction analysis 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. 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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-4121035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280817890,"identity":"e876d73d-e240-49ef-b790-da3d2fb4ac08","order_by":0,"name":"Zhiheng Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACfvb2Awc+8NjI8bM3HyBOi2TPmcSHM2TSjCV7jiUQp8XghoOxMY/NocQNM3IMiHTZDYY0yRk5Bxg38Jz5eOMNg52cbgMBHYyzG49JfDhzh9mcvXez5RyGZGOzAwS0MMscSJOc2fOMzbLn7DZpHoYDidsIaWGTSDCT5v13mMfgRs4z4rTwSCQAvc9zWAKohY04LRI8oEDmSTMABrKx5RwDIvxifxwSlfX97M0Pb7ypsJMjqAXNSmKjBkkLqTpGwSgYBaNgRAAAB35JFmkuGtAAAAAASUVORK5CYII=","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhiheng","middleName":"","lastName":"Zhu","suffix":""},{"id":280817891,"identity":"1027acc2-1a9f-49cf-a22f-ac7eb15de28f","order_by":1,"name":"Zhengwang Xu","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhengwang","middleName":"","lastName":"Xu","suffix":""},{"id":280817892,"identity":"2fedbbd2-6663-4f45-838b-20fd578c7ee8","order_by":2,"name":"Runyang Xiao","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Runyang","middleName":"","lastName":"Xiao","suffix":""},{"id":280817893,"identity":"3433e1db-66dc-4796-b160-fe2331391736","order_by":3,"name":"Zhou Huang","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-03-18 07:47:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4121035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4121035/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72524826,"identity":"589db059-5aa8-4b5b-9e20-e2f6531fc96d","added_by":"auto","created_at":"2024-12-28 15:23:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1570258,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonthePredictionMethodofMOVDeteriorationStateBasedonPrincipalComponentAnalysisandGridSearchOptimizedSupportVectorMachineRegressionAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121035/v1_covered_e0889875-5800-46bf-81bb-b17409d745ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Prediction Method of MOV Deterioration State Based on Principal Component Analysis and Grid Search- Optimized Support Vector Machine Regression Algorithm","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":"Metal oxide varistor, Principal Component Analysis, Grid Search, Support Vector Machine, Continuous pulse impact, prediction analysis","lastPublishedDoi":"10.21203/rs.3.rs-4121035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4121035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAccurate diagnosis of faults in metal oxide varistors (MOV) is crucial for the safe operation of power systems, and the deterioration of MOV under continuous pulse impacts can be more severe. To effectively improve the fault diagnosis rate, this paper proposes a fault diagnosis algorithm based on Principal Component Analysis (PCA) and Grid Search-optimized Support Vector Regression (GS-SVR).Objective, The objective of this study is to propose an effective fault diagnosis algorithm that accurately predicts the fault state of MOV under single and continuous pulse impacts, while reducing the correlation between indication indicators through dimensionality reduction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThe proposed experiment involves conducting a comparative test on MOV with different time intervals between impacts, on the order of 10 seconds. The data collected from this experiment, with a time resolution of 10 seconds, will be subjected to dimensionality reduction using PCA to reduce the correlation between the original indicators. Finally, the GS-SVR model will be employed to analyze and predict the effects of single and continuous pulse impacts on MOV.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Experimental results demonstrate that the GS-SVR model achieves a mean square error of less than 0.00057 in predicting single pulse impacts and still exhibits certain effectiveness for irregular pulse impacts, such as continuous pulses.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The proposed fault diagnosis algorithm based on PCA and GS-SVR can effectively improve the fault diagnosis rate of MOV, and accurately predict the fault state of MOV under single and continuous impulse shock. This is of great significance to the safe operation of power system.\u003c/p\u003e","manuscriptTitle":"Research on the Prediction Method of MOV Deterioration State Based on Principal Component Analysis and Grid Search- Optimized Support Vector Machine Regression Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 02:42:32","doi":"10.21203/rs.3.rs-4121035/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"bdadc374-2956-4811-bbef-0cd1d686b73a","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-28T15:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 02:42:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4121035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4121035","identity":"rs-4121035","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}