The Research on Chain Fault Prediction in AC-DC Hybrid Power Grids Based on Deep Learning

The Research on Chain Fault Prediction in AC-DC Hybrid Power Grids Based on Deep Learning | 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 The Research on Chain Fault Prediction in AC-DC Hybrid Power Grids Based on Deep Learning Wenchao Qin, Xufei Liu, Yiran Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8721157/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Discover Computing → Version 1 posted 5 You are reading this latest preprint version Abstract Accurate prediction of cascading faults in power grids is critical for ensuring their stability and preventing large-scale outages. This paper presents Hybrid Graph-Temporal Transformer (HGTT), for predicting cascading faults in AC/DC hybrid power grids. The HGTT model integrates Graph Attention Networks (GAT) and Temporal Transformers, effectively capturing both spatial and temporal dependencies. Key innovations include an attention mechanism that accounts for electrical distances between nodes, and a causal attention-based temporal feature extraction module. Additionally, two self-supervised tasks reduce reliance on labeled data. Experimental results show that HGTT improves prediction accuracy by over 15% and reduces labeled data requirements by 50%. Cascading Faults AC/DC Hybrid Power Grid Deep Learning Graph Attention Network Temporal Transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Discover Computing → Version 1 posted Reviewers invited by journal 02 Feb, 2026 Editor invited by journal 29 Jan, 2026 Editor assigned by journal 28 Jan, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 28 Jan, 2026 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-8721157","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584788918,"identity":"51769442-574f-4a18-8bf1-a72e6af12f22","order_by":0,"name":"Wenchao Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYDACZhBRISFn38DAIMHAYAMSAjIPENJyxsbYgAGsJY2BgY2RgBYQYGxLS9wA0XKYsBa+47zHJH6wHWbcLpF88MaPmvNyBvcbGx98OMMgzy+GXZ/kYb40yR6ew8yWM9KSLXuO3TY2OMbYbDjjBoPhzNkJWLUYHOYxu80gcZiN4UaOmTRjw+3EDccY26R5PjAkGNzGpwVIMtzI/wbUco5YLQlpEgY3ctiAWg5AtdzArUXyMI/5z54DNgaSPc+MgX5JNpY8lgj0yxkJnH7hO3/G2ODnP4n6fvbkh8AQs5PjO3z44IMPx2zk+aWxa0FEgACqAgnsylG08B/ArWgUjIJRMApGNgAAuCllP5zTkXEAAAAASUVORK5CYII=","orcid":"","institution":"Power Dispatching and Control Center of Yunnan Power Grid Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Wenchao","middleName":"","lastName":"Qin","suffix":""},{"id":584788919,"identity":"359338ed-35e9-4225-931d-b174a570204b","order_by":1,"name":"Xufei Liu","email":"","orcid":"","institution":"Power Dispatching and Control Center of Yunnan Power Grid Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xufei","middleName":"","lastName":"Liu","suffix":""},{"id":584788920,"identity":"2c3f4f83-98c5-490e-ab40-a2969f998c57","order_by":2,"name":"Yiran Tao","email":"","orcid":"","institution":"Power Dispatching and Control Center of Yunnan Power Grid Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yiran","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2026-01-28 13:09:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8721157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8721157/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10791-026-10130-z","type":"published","date":"2026-04-30T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":108437705,"identity":"1af2f356-ede5-4307-85f2-940ae6608bb7","added_by":"auto","created_at":"2026-05-04 16:02:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":619081,"visible":true,"origin":"","legend":"","description":"","filename":"QWCDiscoverComputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8721157/v1_covered_7d152537-be68-4e02-b72f-b67740280e50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Research on Chain Fault Prediction in AC-DC Hybrid Power Grids Based on Deep Learning","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cascading Faults, AC/DC Hybrid Power Grid, Deep Learning, Graph Attention Network, Temporal Transformer","lastPublishedDoi":"10.21203/rs.3.rs-8721157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8721157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate prediction of cascading faults in power grids is critical for ensuring their stability and preventing large-scale outages. 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