Fault Detection, Classification and Localization in Power Transmission Lines Using ANN

Fault Detection, Classification and Localization in Power Transmission Lines Using ANN | 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 Fault Detection, Classification and Localization in Power Transmission Lines Using ANN Živko Sokolović, Mileta Žarković This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5951556/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 Power transmission line is key equipment in secure and reliable power flow in each power system. To arise reliability and security of overhead power lines, different types of failures should be simulated to minimize their impact and to detect and resolve them as quickly as possible. The objective of this paper is to provide an accurate method for detection, classification and localization of faults occurring in power transmission lines using Artificial Neural Network (ANN). Power transmission system was modelled in DIgSILENT PowerFactory, simulating both normal and fault scenarios. Three types of faults were considered for simulation: single-phase-to-ground fault, two-phase short circuit, and three-phase short circuit. Each fault was simulated across the 110 kV power lines with a resolution of 5%. In addition to the fault scenarios, normal scenario was carried out using a load flow analysis, where the system’s load was varied. Voltage and current data from these simulations were utilized to train and test the ANN model. Principal Component Analysis (PCA) was applied for dimensionality reduction, improving the efficiency and performance of the ANN model. The proposed model achieved an accuracy of 100% in detecting fault types, a fault classification accuracy of 94% for identifying the fault line, and a mean absolute error (MAE) of 1.15 in pinpointing the exact fault position. These results demonstrate the model's effectiveness in accurately identifying and localizing faults in power transmission lines, significantly contributing to the reliability and stability of power grid operations. ANN transmission line DIgSILENT fault classification and localization PCA 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-5951556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411058697,"identity":"67cc1e7a-2dfc-426e-bdf2-60a2eb58a382","order_by":0,"name":"Živko Sokolović","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3QMWrDMBSA4RcE6vIO8EzBusIzgkAghB4lxmAvDe3oyTgI7KW0q6HQO/QGMoJ06SEKga711KlDXQfaoXFCtkL1axN8epIAfL6/mOjXVwSiBGAI5QlkMhA9EHsM/RCAuDxGVI2aOyjCoF6vt3i9yG7PzGbbwfxKXewnE4M6bsDpc2yNRk5WFW4ytpDOHkdGCSETh2DjB4qroGG7quhyShYcR+V+Inek+CaZVG/vBwkK4RIEEd/3hDq2S0koB6JGnk89iRp2OrhpDXecRBWmU3rmlHnsx+5aQ11ehPRk2pflx0Kp2r1Sns9ZjVxs1+8D+53+207t8BSfz+f7R30CvW1OnenW0MMAAAAASUVORK5CYII=","orcid":"","institution":"University of Belgrade - School of Electrical Engineering","correspondingAuthor":true,"prefix":"","firstName":"Živko","middleName":"","lastName":"Sokolović","suffix":""},{"id":411058699,"identity":"650909c5-e4a3-4a71-8717-50c8783153e9","order_by":1,"name":"Mileta Žarković","email":"","orcid":"","institution":"University of Belgrade - School of Electrical Engineering","correspondingAuthor":false,"prefix":"","firstName":"Mileta","middleName":"","lastName":"Žarković","suffix":""}],"badges":[],"createdAt":"2025-02-03 13:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5951556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5951556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75925257,"identity":"b5e6766b-80bf-4dfa-99f5-ea957ce86d98","added_by":"auto","created_at":"2025-02-10 15:10:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":853781,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5951556/v1_covered_db6546d0-ba66-4e8f-a69c-0f1512aa829a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fault Detection, Classification and Localization in Power Transmission Lines Using ANN","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":"ANN, transmission line, DIgSILENT, fault classification and localization, PCA","lastPublishedDoi":"10.21203/rs.3.rs-5951556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5951556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePower transmission line is key equipment in secure and reliable power flow in each power system. To arise reliability and security of overhead power lines, different types of failures should be simulated to minimize their impact and to detect and resolve them as quickly as possible. The objective of this paper is to provide an accurate method for detection, classification and localization of faults occurring in power transmission lines using Artificial Neural Network (ANN). Power transmission system was modelled in DIgSILENT PowerFactory, simulating both normal and fault scenarios. Three types of faults were considered for simulation: single-phase-to-ground fault, two-phase short circuit, and three-phase short circuit. Each fault was simulated across the 110 kV power lines with a resolution of 5%. In addition to the fault scenarios, normal scenario was carried out using a load flow analysis, where the system\u0026rsquo;s load was varied. Voltage and current data from these simulations were utilized to train and test the ANN model. Principal Component Analysis (PCA) was applied for dimensionality reduction, improving the efficiency and performance of the ANN model. The proposed model achieved an accuracy of 100% in detecting fault types, a fault classification accuracy of 94% for identifying the fault line, and a mean absolute error (MAE) of 1.15 in pinpointing the exact fault position. 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