Smart Fault Detection in Satellite Electrical Power System

Smart Fault Detection in Satellite Electrical Power System | 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 Smart Fault Detection in Satellite Electrical Power System Niloofar Nobahari, Alireza Rezaeee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7595285/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract This paper presents an new approach for detecting in the electrical power system of satellites operating in Low Earth Orbit (LEO) without an Attitude Determination and Control Subsystem (ADCS). Components of these systems are prone to faults, such as line-to-line faults in the photovoltaic subsystem, open circuits, and short circuits in the DC-to-DC converter, as well as ground faults in batteries. In the previous research has largely focused on detecting faults in each components, such as photovoltaic arrays or converter systems, therefore, has been limited attention given to whole electrical power system of satellite as a whole system. Our approach addresses this gap by utilizing a Multi-Layer Perceptron (MLP) neural network model, which leverages input data such as solar radiation and surface temperature to predict current and load outputs. These machine learning techniques that classifiy use different approaches like Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN), to classify faults effectively. The model presented achieves over 99% accuracy in identifying faults across multiple subsystems, marking a notable advancement from previous approaches by offering a complete diagnostic solution for the entire satellite power system. This thorough method boosts system reliability and helps lower the chances of mission failure. Fault diagnosis Machine learning Satellite Electrical power system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor assigned by journal 13 Sep, 2025 Submission checks completed at journal 13 Sep, 2025 First submitted to journal 11 Sep, 2025 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-7595285","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544864866,"identity":"de0aeb0f-355e-4283-a162-f633ecff0cc8","order_by":0,"name":"Niloofar Nobahari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYHACNgYGAwkefiAJ5DATq6XCRkaygTQtZ9JsDA4Qq8VcuvnYg59th3mMjzdvk2CosE5sIKTFcs6xdMNeoBazM8fKJBjOpBPWYnAjx0yCF6QFxGBsO0ycFsm/IIfNAGn5R6QWaZ4zaTwGEiAtDURpSUs3lqmw4ZE4c6zYIuFYujERWpKPPXxjIGHP39688caHGmtZglpQQQJpykfBKBgFo2AU4AIAM3A8ywlwnboAAAAASUVORK5CYII=","orcid":"","institution":"Old Dominion University","correspondingAuthor":true,"prefix":"","firstName":"Niloofar","middleName":"","lastName":"Nobahari","suffix":""},{"id":544864867,"identity":"1b35957f-8ce2-4672-ab26-5fd02be30e12","order_by":1,"name":"Alireza Rezaeee","email":"","orcid":"","institution":"University of Tehran faculty of Applied Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Rezaeee","suffix":""}],"badges":[],"createdAt":"2025-09-12 00:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7595285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7595285/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96315586,"identity":"5e972f07-38b6-48ac-83b5-09381a03a3e7","added_by":"auto","created_at":"2025-11-19 17:31:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1082988,"visible":true,"origin":"","legend":"","description":"","filename":"Paper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7595285/v1/8144e709a2284e41f8085ad0.pdf"},{"id":96315585,"identity":"39dd4e73-a076-4013-992e-25bc47bba1a5","added_by":"auto","created_at":"2025-11-19 17:31:46","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3864,"visible":true,"origin":"","legend":"","description":"","filename":"ba5fda789a784cf680d691e8fdef2a0e.json","url":"https://assets-eu.researchsquare.com/files/rs-7595285/v1/8d8bb5b79b84ea2160d507a2.json"},{"id":96365426,"identity":"211b3433-51a5-4458-9950-ad3d3d39ee98","added_by":"auto","created_at":"2025-11-20 10:10:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1080399,"visible":true,"origin":"","legend":"","description":"","filename":"Paper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7595285/v1_covered_ced41133-ce8d-444a-ada2-3220fcc6d3cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSmart Fault Detection in Satellite Electrical Power System\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"iranian-journal-of-science-and-technology-transactions-of-electrical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iste","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Electrical Engineering](https://link.springer.com/journal/40998)","snPcode":"40998","submissionUrl":"https://submission.springernature.com/new-submission/40998/3","title":"Iranian Journal of Science and Technology, Transactions of Electrical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fault diagnosis, Machine learning, Satellite, Electrical power system","lastPublishedDoi":"10.21203/rs.3.rs-7595285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7595285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper presents an new approach for detecting in the electrical power system of satellites operating in Low Earth Orbit (LEO) without an Attitude Determination and Control Subsystem (ADCS). 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