Research on series arc fault detection methodHousehold loads based on voltage signals

Research on series arc fault detection methodHousehold loads based on voltage signals | 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 Research on series arc fault detection methodHousehold loads based on voltage signals Bin Li, Jiahui Shu, Feifan Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4349309/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces Inception with multi-scale parallel convolution operation, and combines Bidirectional Long Short-Term Memory Recurrent Network (BiLSTM) with attention mechanism. Firstly, a household experimental platform was built, and the line voltage signal obtained by the experiment was subjected to wavelet transform and Principal Component Analysis (PCA) dimensionality reduction to construct a dataset. Secondly, Inception is introduced to extract the multi-level features of the samples, and the parallel output is input into BiLSTM after global max pooling layer. Then, self-attention is used to perform reinforcement learning on the hidden state vector. Finally, the output results are classified by the fully connected layer. Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. In addition, additional experiments at different sampling frequencies show that the method has good adaptability, and the identification accuracy has better performance when the sampling frequency is 10KHZ, which has certain theoretical guiding significance for the development of the series arc fault detection device in the next step. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computer science Series arc fault detection Multi-scale parallel convolution operation Wavelet transform Principal component analysis dimensionality reduction Bidirectional Long Short-Term Memory Recurrent Network Self-attention Full Text Additional Declarations No competing interests reported. Supplementary Files datad1.xlsx 1.xlsx 2.xlsx Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Nov, 2024 Reviews received at journal 24 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers invited by journal 12 Jul, 2024 Editor assigned by journal 04 Jul, 2024 Editor invited by journal 07 May, 2024 Submission checks completed at journal 06 May, 2024 First submitted to journal 30 Apr, 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. 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-4349309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299698690,"identity":"9d670ea8-1161-48f0-aa13-3e529d9b87ca","order_by":0,"name":"Bin Li","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Li","suffix":""},{"id":299698691,"identity":"051841c5-2b6a-4dbf-9b1b-ae3d3ec0cf97","order_by":1,"name":"Jiahui Shu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACNmaG5AcJFTZy/PKHDxCnhY+94ZnBhzNpxpIz2BKI0yLHc/CB5MyWw4kbbvAYEOkwieQEY96Gw4wzZ/d8vPGGwU5Ot4GglrSEx7w70pn5Zc5utpzDkGxsdoCglhygLWes2SQbcrdJ8zAcSNxGWEv+B2neNmYegwM5z4jUwnMgQXJmm7OEwY0cNiK1sDekgQLZQLLnmLHlHAMi/CLfDInK+n725oc33lTYyRHUggIkiI0aZC2k6hgFo2AUjIIRAQD6VUQZZTanEQAAAABJRU5ErkJggg==","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":true,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Shu","suffix":""},{"id":299698692,"identity":"96ba12c4-af67-4f43-8807-45d8dce47271","order_by":2,"name":"Feifan Cui","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Feifan","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2024-04-30 13:10:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4349309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4349309/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12760-7","type":"published","date":"2025-07-27T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87756700,"identity":"5bf24dcc-9015-47ff-be7d-7e5cf9b90fd2","added_by":"auto","created_at":"2025-07-28 16:07:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":820164,"visible":true,"origin":"","legend":"","description":"","filename":"document.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349309/v1_covered_066182c8-03f5-484e-b2b7-df54aec0fbcf.pdf"},{"id":56215119,"identity":"d3bb24ee-e0e2-43fd-85e1-2ebecaf8e8b5","added_by":"auto","created_at":"2024-05-10 02:41:56","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":51539,"visible":true,"origin":"","legend":"","description":"","filename":"datad1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4349309/v1/5753bee38d6619640d27df1a.xlsx"},{"id":56216045,"identity":"570b140c-6b4c-4ae1-904c-758d9cf9252c","added_by":"auto","created_at":"2024-05-10 02:57:58","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9424,"visible":true,"origin":"","legend":"","description":"","filename":"1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4349309/v1/8520be17ef21e04cb9fb9ee6.xlsx"},{"id":56215121,"identity":"81eb4c9a-8e38-4df3-a665-f42f6662cc47","added_by":"auto","created_at":"2024-05-10 02:41:58","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9763,"visible":true,"origin":"","legend":"","description":"","filename":"2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4349309/v1/ab87eb7d3fa8663f269687e3.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on series arc fault detection methodHousehold loads based on voltage signals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Series arc fault detection, Multi-scale parallel convolution operation, Wavelet transform, Principal component analysis dimensionality reduction, Bidirectional Long Short-Term Memory Recurrent Network, Self-attention","lastPublishedDoi":"10.21203/rs.3.rs-4349309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4349309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces Inception with multi-scale parallel convolution operation, and combines Bidirectional Long Short-Term Memory Recurrent Network (BiLSTM) with attention mechanism. Firstly, a household experimental platform was built, and the line voltage signal obtained by the experiment was subjected to wavelet transform and Principal Component Analysis (PCA) dimensionality reduction to construct a dataset. Secondly, Inception is introduced to extract the multi-level features of the samples, and the parallel output is input into BiLSTM after global max pooling layer. Then, self-attention is used to perform reinforcement learning on the hidden state vector. Finally, the output results are classified by the fully connected layer. Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. In addition, additional experiments at different sampling frequencies show that the method has good adaptability, and the identification accuracy has better performance when the sampling frequency is 10KHZ, which has certain theoretical guiding significance for the development of the series arc fault detection device in the next step.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Research on series arc fault detection methodHousehold loads based on voltage signals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 02:41:37","doi":"10.21203/rs.3.rs-4349309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-19T08:38:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-24T21:10:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143594344197117285352179234714100737365","date":"2024-07-15T23:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-13T03:19:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T17:26:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-07T11:12:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-06T04:16:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-30T13:08:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a0baaab-3bf2-4dfc-a5d0-423c89f6b4f1","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31619143,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":31619144,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-07-28T16:01:41+00:00","versionOfRecord":{"articleIdentity":"rs-4349309","link":"https://doi.org/10.1038/s41598-025-12760-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-27 15:57:42","publishedOnDateReadable":"July 27th, 2025"},"versionCreatedAt":"2024-05-10 02:41:37","video":"","vorDoi":"10.1038/s41598-025-12760-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-12760-7","workflowStages":[]},"version":"v1","identity":"rs-4349309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4349309","identity":"rs-4349309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}