Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN

Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN | 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 Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN Ravinder Scholar, Vikram Kulkarni Assistant professor (Senior) This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869400/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2025 Read the published version in Smart Grids and Sustainable Energy → Version 1 posted 10 You are reading this latest preprint version Abstract The integration of Information and Communication Technologies (ICT) into the conventional power grid defines a smart grid, overseeing electrical power distribution, generation, and utilization. Despite its benefits, the smart grid encounters communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failure, structural faults, power consumption irregularities, and cyber-attacks. While power systems adeptly handle natural disturbances, discerning cyber-attack-induced anomalies proves complex. This paper introduces an intelligent deep learning approach for smart grid anomaly detection. Initially, data is collected from standard smart meter, weather, and user behavior sources. Optimal weighted feature selection, utilizing the Modified Flow Direction Algorithm (MFDA), precedes inputting selected features into the "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results affirm the model's superior performance, with a heightened detection rate compared to existing methods, bolstering the smart grid system's robustness. Anomaly Detection Smart Grid Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit Modified Flow Direction Algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2025 Read the published version in Smart Grids and Sustainable Energy → Version 1 posted Editorial decision: Revision requested 06 Jun, 2024 Reviews received at journal 16 May, 2024 Reviewers agreed at journal 13 May, 2024 Reviews received at journal 13 Apr, 2024 Reviewers agreed at journal 30 Mar, 2024 Reviewers agreed at journal 15 Mar, 2024 Reviewers invited by journal 02 Feb, 2024 Editor assigned by journal 28 Jan, 2024 Submission checks completed at journal 24 Jan, 2024 First submitted to journal 16 Jan, 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-3869400","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268962398,"identity":"65ed9fe3-b40d-4ad3-8831-4b60b3f3c095","order_by":0,"name":"Ravinder Scholar","email":"","orcid":"","institution":"MPSTME, SVKM’s NMIMS University","correspondingAuthor":false,"prefix":"","firstName":"Ravinder","middleName":"","lastName":"Scholar","suffix":""},{"id":268962399,"identity":"ea3de6c8-f219-429d-bc0b-4db352f418bf","order_by":1,"name":"Vikram Kulkarni Assistant professor (Senior)","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYDACZjYwZcDADmZIyDEwMDaAWBASrxYIQ8KYsBYGVC0MiTCVOLUYHGdL+/Azh8HY4DBb4ufKHRbp28UOt278wWAju+EADi2H2Q7P7N3GYAZiSJ49I5G7c3Zi220ehjRjXFokm9mbGXi3MdgYHGZvkGxsk8jdcBuohYHhcCI+LYx/IVqafwK1pBsAtdz8wfAfpxZ+ZrbDzLwQhx0D2ZIA0nKDh+EAPi3JzLLbJIwlD7OlWQK1GIIdxmOQbDwThxY2/mPGjG+32Rj2HW8zvtnYVidvcDv92c0fFXayfTi0QIEEuoABXuWjYBSMglEwCggAADsPWqo3sAzzAAAAAElFTkSuQmCC","orcid":"","institution":"MPSTME, SVKM’s NMIMS University","correspondingAuthor":true,"prefix":"","firstName":"Vikram","middleName":"Kulkarni Assistant professor","lastName":"(Senior)","suffix":""}],"badges":[],"createdAt":"2024-01-16 09:53:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3869400/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3869400/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40866-024-00216-2","type":"published","date":"2025-01-16T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74284558,"identity":"68d237de-6a1d-4db5-b568-50e469a0f8f9","added_by":"auto","created_at":"2025-01-20 16:08:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1054905,"visible":true,"origin":"","legend":"","description":"","filename":"Ravinder.M.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3869400/v1_covered_299c04a3-b32b-490d-a85e-9aa5121d0cf2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN","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":"technology-and-economics-of-smart-grids-and-sustainable-energy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tesg","sideBox":"Learn more about [Smart Grids and Sustainable Energy](https://www.springer.com/journal/40866)","snPcode":"40866","submissionUrl":"https://submission.nature.com/new-submission/40866/3","title":"Smart Grids and Sustainable Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Anomaly Detection, Smart Grid, Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit, Modified Flow Direction Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-3869400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3869400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of Information and Communication Technologies (ICT) into the conventional power grid defines a smart grid, overseeing electrical power distribution, generation, and utilization. Despite its benefits, the smart grid encounters communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failure, structural faults, power consumption irregularities, and cyber-attacks. While power systems adeptly handle natural disturbances, discerning cyber-attack-induced anomalies proves complex. This paper introduces an intelligent deep learning approach for smart grid anomaly detection. Initially, data is collected from standard smart meter, weather, and user behavior sources. Optimal weighted feature selection, utilizing the Modified Flow Direction Algorithm (MFDA), precedes inputting selected features into the \"Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)\" for anomaly identification. Simulation results affirm the model's superior performance, with a heightened detection rate compared to existing methods, bolstering the smart grid system's robustness.\u003c/p\u003e","manuscriptTitle":"Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 17:47:38","doi":"10.21203/rs.3.rs-3869400/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-06T10:26:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-16T04:18:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232758796302544560104872188901819381438","date":"2024-05-13T09:49:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-13T16:15:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70201ecc-1f86-4ab0-b484-767df2d6497a","date":"2024-03-31T02:11:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267f2922-988e-445c-b42a-5801009c9d52","date":"2024-03-15T16:25:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-02T10:38:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-28T10:20:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-24T07:43:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Smart Grids and Sustainable Energy","date":"2024-01-16T09:52:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"technology-and-economics-of-smart-grids-and-sustainable-energy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tesg","sideBox":"Learn more about [Smart Grids and Sustainable Energy](https://www.springer.com/journal/40866)","snPcode":"40866","submissionUrl":"https://submission.nature.com/new-submission/40866/3","title":"Smart Grids and Sustainable Energy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"182cb983-f257-43b8-862d-00db3a726d61","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:00:28+00:00","versionOfRecord":{"articleIdentity":"rs-3869400","link":"https://doi.org/10.1007/s40866-024-00216-2","journal":{"identity":"technology-and-economics-of-smart-grids-and-sustainable-energy","isVorOnly":false,"title":"Smart Grids and Sustainable Energy"},"publishedOn":"2025-01-16 15:57:15","publishedOnDateReadable":"January 16th, 2025"},"versionCreatedAt":"2024-01-25 17:47:38","video":"","vorDoi":"10.1007/s40866-024-00216-2","vorDoiUrl":"https://doi.org/10.1007/s40866-024-00216-2","workflowStages":[]},"version":"v1","identity":"rs-3869400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3869400","identity":"rs-3869400","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}