Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control | 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 Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control Mohamed Meddah, Ahmed Wahid Belarbi, Karim Negadi, Younes Djaballah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4080085/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 This article conducts a thorough comparison of three Maximum Power Point Tracking control techniques for photovoltaic systems: Perturb and Observe, Incremental Conductance, and Artificial Neural Network. The study aims to identify the most effective MPPT method by subjecting each technique to numerical simulations. The article explores the performance, efficiency, and robustness of Perturb and Observe, Incremental Conductance and Artificial Neural Network in capturing the maximum power output from photovoltaic panels under varying environmental conditions. Following rigorous testing through numerical simulations, the superior technique is selected for implementation in a grid-connected photovoltaic power conversion chain. This research contributes valuable insights into the optimization of photovoltaic system performance through advanced MPPT control strategies, facilitating informed decisions for practical applications in renewable energy systems. Artificial Neural Network MPPT Control power converter Emulti-level inverter photovoltaic conversion chain 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. 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-4080085","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281563378,"identity":"c2708821-7669-4aa0-8770-b8b0e96253a7","order_by":0,"name":"Mohamed Meddah","email":"","orcid":"","institution":"Laboratory of Physics and Electrical Discharge, Electrotechnical Engineering Laboratory, University of Sciences and Technology of Oran","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Meddah","suffix":""},{"id":281563379,"identity":"d896997d-b609-4f02-af2e-c641838aad4f","order_by":1,"name":"Ahmed Wahid Belarbi","email":"","orcid":"","institution":"Laboratory of Physics and Electrical Discharge, Electrotechnical Engineering Laboratory, University of Sciences and Technology of Oran","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Wahid","lastName":"Belarbi","suffix":""},{"id":281563380,"identity":"e3ac8fd2-4481-432e-bf98-d5b2d390c7e0","order_by":2,"name":"Karim Negadi","email":"","orcid":"","institution":"L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, BP 78 Zaaroura, 14000","correspondingAuthor":false,"prefix":"","firstName":"Karim","middleName":"","lastName":"Negadi","suffix":""},{"id":281563381,"identity":"be566469-4e88-4241-ad66-1f584b192bcb","order_by":3,"name":"Younes Djaballah","email":"data:image/png;base64,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","orcid":"","institution":"Applied automation and diagnostic industrial laboratory (LAADI), Ziane Achour University of Djelfa, BP 3117","correspondingAuthor":true,"prefix":"","firstName":"Younes","middleName":"","lastName":"Djaballah","suffix":""}],"badges":[],"createdAt":"2024-03-12 04:30:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4080085/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4080085/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53269188,"identity":"7ea849cb-0709-490d-8d06-dc9e7f80f43f","added_by":"auto","created_at":"2024-03-22 16:08:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4197781,"visible":true,"origin":"","legend":"","description":"","filename":"SmartGridsandSustainableEnergy4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4080085/v1_covered_6c6b3bf7-0b0b-455a-85df-8f47852c9e16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control","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":"Artificial Neural Network, MPPT Control, power converter, Emulti-level inverter, photovoltaic conversion chain","lastPublishedDoi":"10.21203/rs.3.rs-4080085/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4080085/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article conducts a thorough comparison of three Maximum Power Point Tracking control techniques for photovoltaic systems: Perturb and Observe, Incremental Conductance, and Artificial Neural Network. The study aims to identify the most effective MPPT method by subjecting each technique to numerical simulations. The article explores the performance, efficiency, and robustness of Perturb and Observe, Incremental Conductance and Artificial Neural Network in capturing the maximum power output from photovoltaic panels under varying environmental conditions. Following rigorous testing through numerical simulations, the superior technique is selected for implementation in a grid-connected photovoltaic power conversion chain. This research contributes valuable insights into the optimization of photovoltaic system performance through advanced MPPT control strategies, facilitating informed decisions for practical applications in renewable energy systems.\u003c/p\u003e","manuscriptTitle":"Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 09:53:18","doi":"10.21203/rs.3.rs-4080085/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"896ed050-cd2b-430e-be84-8e1e863dbd93","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-22T16:00:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 09:53:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4080085","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4080085","identity":"rs-4080085","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}