Critical Analysis and Application of Enhanced Hunter-Prey Algorithm for MPPT in Photovoltaic Systems Under Complex Partial Shading Conditions

Critical Analysis and Application of Enhanced Hunter-Prey Algorithm for MPPT in Photovoltaic Systems Under Complex Partial Shading Conditions | 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 Critical Analysis and Application of Enhanced Hunter-Prey Algorithm for MPPT in Photovoltaic Systems Under Complex Partial Shading Conditions Zhuoxuan Li, Changxin Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4460931/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 The Hunter Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional Maximum Power Point Tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in Improved Hunter Prey Optimization (IHPO) is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. The performance evaluation is conducted utilizing both MATLAB simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. Simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions with a tracking response time of approximately 0.2 seconds. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method's output performance is evaluated on the StarSim RCP platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times. Physical sciences/Energy science and technology/Renewable energy/Solar energy Physical sciences/Engineering/Electrical and electronic engineering PV power generation Partial shading conditions Maximum power point tracking Hunter-Prey optimization algorithm StarSim HIL 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-4460931","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312056057,"identity":"1233d7a6-0f2c-4aaf-8486-a49528ff7743","order_by":0,"name":"Zhuoxuan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJklEQVRIiWNgGAWjYDACCRiDnbHxQUIFhHuAoQBMM+PXwszYbPDgDEyLAVFaGNgkH7bBeHi0yM/uMXzM22aXx8DM3CCROM8i2py99+GBHwYM8vwNPMYGWLQwzjljbDizLbkY6LAGg8RtErk7e44bHOwxYDCccYDHOAGLFmaJHDOJj23MiQ1ALQkgLRtupDEc4DFgYNzAwGN8AIsWNpCWxLZ6sJYDiXOAWu4/Yzj4x4DBHpcWHogth0FaGhsSG0C2sDEcBtqSCNKCzWESEmnFhjPOHU9sAwYyQ8IxkF/SGA7LGEgkzzjMVozN+/Izkjc+5imrTuxnb3/+80dNXe529mPMH99U2Nj2tzdvlsCiBeEpGANqsASuiMQCsLllFIyCUTAKRjYAAA0YXOfnt3LHAAAAAElFTkSuQmCC","orcid":"","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Zhuoxuan","middleName":"","lastName":"Li","suffix":""},{"id":312056058,"identity":"5c0da3f4-448a-472f-b3c0-a90028172dc5","order_by":1,"name":"Changxin Fu","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Changxin","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2024-05-22 12:33:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4460931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4460931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62768570,"identity":"1cd6d26f-41ca-4250-a463-f3d63eb037ad","added_by":"auto","created_at":"2024-08-19 08:52:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3867670,"visible":true,"origin":"","legend":"","description":"","filename":"R1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4460931/v1_covered_6f0fd6ac-2a17-4308-a002-c0290f2bedba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Critical Analysis and Application of Enhanced Hunter-Prey Algorithm for MPPT in Photovoltaic Systems Under Complex Partial Shading Conditions","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":"PV power generation, Partial shading conditions, Maximum power point tracking, Hunter-Prey optimization algorithm, StarSim HIL","lastPublishedDoi":"10.21203/rs.3.rs-4460931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4460931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Hunter Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional Maximum Power Point Tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in Improved Hunter Prey Optimization (IHPO) is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. The performance evaluation is conducted utilizing both MATLAB simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. 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