Parameter Estimation of Photovoltaic Models Based on Enhancement Moutain Gazelle Optimizer Algorithm

Parameter Estimation of Photovoltaic Models Based on Enhancement Moutain Gazelle Optimizer Algorithm | 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 Article Parameter Estimation of Photovoltaic Models Based on Enhancement Moutain Gazelle Optimizer Algorithm Diaa Salama AbdElminaam, Ala Saleh Alluhaidan, Walaa H. Elashmawi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4651513/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 Accurate parameter estimation in photovoltaic (PV) system design and simulationis essential for optimizing performance. Traditional numerical, analytical,and hybrid methods often fail to deliver quick and precise results. This researchintroduces enhancements to three fundamental PV models (single-diode, doublediode,and three-diode) and employs the Improved Mountain Gazelle Optimizer(i_MGO) algorithm for parameter extraction. The innovative objective functionproposed aids in minimizing the discrepancy between calculated and measuredvalues. Rigorous experimental validation demonstrates that i_MGO outperformsexisting algorithms, achieving optimal parameter values with minimal rootmean square error (RMSE). The experimental findings illustrate that i_MGOperforms better than the following competing algorithms: Improved MountainGazelle Optimizer (i_MGO), Harris Hawks Optimization (HHO), LightningAttachment Procedure, Optimization Algorithm (LAPO), Sine Cosine Algorithm(SCA), Grey Wolf Optimizer (GWO), African Vultures Optimization Algorithm(AVOA), Hippopotamus Optimization Algorithm (HO), Electric Eel ForagingOptimization (EEFO), Synergistic Swarm Optimization Algorithm (SSOA),Coati Optimization Algorithm (COA), Gazelle Optimization Algorithm (GOA).This comparison demonstrates that the parameters extracted by i_MGO areoptimal, as the discrepancy between measured and calculated data is minimal.The optimal RMSE values for SDM, DDM, and TDM, as determined bythe proposed i_MGO algorithm, are 0.00081373, 0.00073908, and 0.00092975,correspondingly. Physical sciences/Engineering/Energy infrastructure Physical sciences/Energy science and technology Physical sciences/Energy science and technology/Renewable energy Enhancement Moutain Gazelle Optimizer Algorithm (i_MGO) PV parameter estimation Single diode model Double diode model Three diode model Solar energy 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. 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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-4651513","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329138817,"identity":"665499c5-0563-4401-8d35-2baaec9b39be","order_by":0,"name":"Diaa Salama AbdElminaam","email":"data:image/png;base64,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","orcid":"","institution":"Benha University","correspondingAuthor":true,"prefix":"","firstName":"Diaa","middleName":"Salama","lastName":"AbdElminaam","suffix":""},{"id":329138818,"identity":"ef94c47d-a802-45a8-aa68-0e444ed3991e","order_by":1,"name":"Ala Saleh Alluhaidan","email":"","orcid":"","institution":"Princess Nourah bint Abdulrahman University","correspondingAuthor":false,"prefix":"","firstName":"Ala","middleName":"Saleh","lastName":"Alluhaidan","suffix":""},{"id":329138820,"identity":"a6736833-47e3-4111-8890-f4bb4bf7d0aa","order_by":2,"name":"Walaa H. 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Traditional numerical, analytical,and hybrid methods often fail to deliver quick and precise results. This researchintroduces enhancements to three fundamental PV models (single-diode, doublediode,and three-diode) and employs the Improved Mountain Gazelle Optimizer(i_MGO) algorithm for parameter extraction. The innovative objective functionproposed aids in minimizing the discrepancy between calculated and measuredvalues. Rigorous experimental validation demonstrates that i_MGO outperformsexisting algorithms, achieving optimal parameter values with minimal rootmean square error (RMSE). The experimental findings illustrate that i_MGOperforms better than the following competing algorithms: Improved MountainGazelle Optimizer (i_MGO), Harris Hawks Optimization (HHO), LightningAttachment Procedure, Optimization Algorithm (LAPO), Sine Cosine Algorithm(SCA), Grey Wolf Optimizer (GWO), African Vultures Optimization Algorithm(AVOA), Hippopotamus Optimization Algorithm (HO), Electric Eel ForagingOptimization (EEFO), Synergistic Swarm Optimization Algorithm (SSOA),Coati Optimization Algorithm (COA), Gazelle Optimization Algorithm (GOA).This comparison demonstrates that the parameters extracted by i_MGO areoptimal, as the discrepancy between measured and calculated data is minimal.The optimal RMSE values for SDM, DDM, and TDM, as determined bythe proposed i_MGO algorithm, are 0.00081373, 0.00073908, and 0.00092975,correspondingly.","manuscriptTitle":"Parameter Estimation of Photovoltaic Models Based on Enhancement Moutain Gazelle Optimizer Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 10:51:46","doi":"10.21203/rs.3.rs-4651513/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":"18a8f561-8ed6-4f05-b4c4-9a23cf918637","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34845668,"name":"Physical sciences/Engineering/Energy infrastructure"},{"id":34845669,"name":"Physical sciences/Energy science and technology"},{"id":34845670,"name":"Physical sciences/Energy science and technology/Renewable energy"}],"tags":[],"updatedAt":"2025-04-24T10:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 10:51:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4651513","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4651513","identity":"rs-4651513","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}