A New War Strategy Optimization Algorithm based Maximum Power Point Tracking Method for PV Systems under Partial Shading Conditions

A New War Strategy Optimization Algorithm based Maximum Power Point Tracking Method for PV Systems under 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 A New War Strategy Optimization Algorithm based Maximum Power Point Tracking Method for PV Systems under Partial Shading Conditions Muhannad Alshareef This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6213252/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 May, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Solar energy systems are well known for their eco-nature and cost-effectiveness as they gain more traction with lower installation expenses and enhanced efficiency levels over time. Traditional maximum power point tracking (MPPT) methods perform effectively in uniform irradiance conditions but encounter difficulties in identifying global maximum power points (GMPP) during partial shading conditions (PSCs). Although various advanced methods exist to tackle this challenge such as meta heuristic approaches it is evident that there is potential for further enhancement, in accelerating the convergence process towards the GMPP. This study presents an approach for maximizing power point tracking (MPPT) using War Strategy Optimization (WSO) which imitates the tactical movements of military forces in combat situations. The optimization procedure imitates battlefield tactics by having soldiers adapt their positions in time to reach an optimal outcome. Two primary war tactics—attack and defensive—are simulated within this model. In order to improve the effectiveness and resilience of the algorithm a novel weight adjustment mechanism and a strategy for relocating soldiers have been integrated. The effectiveness of the WSO algorithm was tested with more than 25 benchmark functions, demonstrating significant improvements in performance compared to well-known metaheuristic algorithms from the existing literature. Th proposed WSO algorithm seems to find a middle ground between exploring and exploiting PSC based photovoltaic systems. The simulation results show that it outperforms sophisticated MPPT techniques. In contrast to AI-based MPPT methods, the proposed WSO demonstrates faster tracking speed, improved dynamic response, higher static and dynamic tracking efficiency, better power tracking, and greater accuracy, even in complex scenarios involving multiple shaded areas. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Energy infrastructure Photovoltaic (PV) system maximum power point tracking (MPPT) war strategy optimization (WSO) DC-DC converter partial shading conditions (PSC) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 May, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Editor invited by journal 20 Mar, 2025 Submission checks completed at journal 19 Mar, 2025 First submitted to journal 12 Mar, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6213252","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":437194954,"identity":"84b4b183-3095-44dc-a526-f3da21243189","order_by":0,"name":"Muhannad Alshareef","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3OIQvCQBTA8SeCKw+sG3P6CQTHwak4/CyKsLRmsTmTZdgFP4R2wQcLK/sAA4siLC0YFYR5isEgt0XD/cO9C+8HD0Cl+suo9h7a0n/Pil+aYEylCXxIMipJ2osoPd33ThONzD4iONaWtPNVRjjFXTtIXYamxwYILtsSMr2A1HSkcHwwPW4ihGNBoIBEqfGgfB4Y8YvkgmiXm5yQ2CQaoY4vQoIALzqMmw2a2AG60/6mM2HrEHlPShJxWEbDFmrhLslmQ2sVLS+JjIBOX3/oiLcq3RfV/W+iUqlUqh89ASfWTHdT3qJWAAAAAElFTkSuQmCC","orcid":"","institution":"Umm al-Qura University","correspondingAuthor":true,"prefix":"","firstName":"Muhannad","middleName":"","lastName":"Alshareef","suffix":""}],"badges":[],"createdAt":"2025-03-12 15:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6213252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6213252/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04733-7","type":"published","date":"2025-05-30T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83783126,"identity":"e533fa89-c7a7-4e9d-ac46-559328b39705","added_by":"auto","created_at":"2025-06-02 16:10:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1604323,"visible":true,"origin":"","legend":"","description":"","filename":"ANewWarStrategyOptimizationAlgorithmbasedMaximu.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6213252/v1_covered_d5d52ae4-07ab-4d8b-afe6-e6b1f7c4d878.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A New War Strategy Optimization Algorithm based Maximum Power Point Tracking Method for PV Systems under Partial Shading Conditions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Photovoltaic (PV) system, maximum power point tracking (MPPT), war strategy optimization (WSO), DC-DC converter, partial shading conditions (PSC)","lastPublishedDoi":"10.21203/rs.3.rs-6213252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6213252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSolar energy systems are well known for their eco-nature and cost-effectiveness as they gain more traction with lower installation expenses and enhanced efficiency levels over time. Traditional maximum power point tracking (MPPT) methods perform effectively in uniform irradiance conditions but encounter difficulties in identifying global maximum power points (GMPP) during partial shading conditions (PSCs). Although various advanced methods exist to tackle this challenge such as meta heuristic approaches it is evident that there is potential for further enhancement, in accelerating the convergence process towards the GMPP.\u003c/p\u003e\n\u003cp\u003eThis study presents an approach for maximizing power point tracking (MPPT) using War Strategy Optimization (WSO) which imitates the tactical movements of military forces in combat situations. The optimization procedure imitates battlefield tactics by having soldiers adapt their positions in time to reach an optimal outcome. Two primary war tactics—attack and defensive—are simulated within this model. In order to improve the effectiveness and resilience of the algorithm a novel weight adjustment mechanism and a strategy for relocating soldiers have been integrated.\u003c/p\u003e\n\u003cp\u003eThe effectiveness of the WSO algorithm was tested with more than 25 benchmark functions, demonstrating significant improvements in performance compared to well-known metaheuristic algorithms from the existing literature.\u003c/p\u003e\n\u003cp\u003eTh proposed WSO algorithm seems to find a middle ground between exploring and exploiting PSC based photovoltaic systems. The simulation results \u0026nbsp;show that it outperforms sophisticated MPPT techniques. 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