Fuzzy Logic Controller and P&O-Based MPPT Techniques for Stand-Alone PV Systems: A Comparison

Fuzzy Logic Controller and P&O-Based MPPT Techniques for Stand-Alone PV Systems: A Comparison | 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 Fuzzy Logic Controller and P&O-Based MPPT Techniques for Stand-Alone PV Systems: A Comparison Rajesh Kumar, Ananyo Bhattacharya, Aanchal Singh S. Vardhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4926323/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 proposed work focuses on photovoltaic (PV) system monitoring and the use of maximum power point tracking (MPPT) techniques for optimal power generation. It emphasizes the widespread application of artificial intelligence (AI) in MPPT methods in solar power systems, which can significantly improve performance and efficiency. However, AI-based MPPT techniques may be more computationally intensive and costly. Hybrid MPPT methods combine traditional and AI techniques to balance performance and complexity, addressing these issues. Fuzzy logic control (FLC) emerges as a viable technique, though synchronization is required for optimal performance under variable irradiation and weather conditions. This paper uses MATLAB/Simulink to compare the four main MPPT techniques: fuzzy-based variable step size P&O, FLC, and hybrid P&O-FLC algorithms. Further, a symmetric fuzzy controller, the perturb-and-observe (P&O) method, and a mixed method that combines both fuzzy and P&O-based controllers were compared. At the same time, many other FLC-based MPPT strategies were also examined. All tests were performed in a solar PV system with a DC-DC boost converter, considering varying atmospheric conditions. Overall, the study's aim is to provide insights into the relative effectiveness of various MPPT techniques, with a particular emphasis on the use of FLC and its hybrids in a variety of environmental scenarios. MPPT technique F-P&O hybrid controller FLC-based VSS- P&O algorithm DC-DC boost converter MATLAB/Simulink 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-4926323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344699016,"identity":"df4a7468-190a-41dc-b4df-b56e9615d172","order_by":0,"name":"Rajesh Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie3RMQrCMBSA4YQH6ZKatVLFKygFHQS9SqTQLt2cxEFFqKOrXiQ4KhlcPIDg0uoJOghmM3VwTDsK5h9CAvlI2iBks/1mHOElIAR4lekVbdQnDshuSUitcz6EkcgrF5VksDnmuToMOwxof/ZMRi2CIL9fDaR14WHgXuLefk37t7YI9cVIECQG4iEe+TiVvCs1aQrQhBLfSFgWK6XJWJNpUyxqEI9HyC1PARLhQsg6JAt9N417OwnSx+JMCVR9C0smhUr1H9ueVoUS8zFz1vnDRPTb8e8U6Gc0bi9zjt8pflXuttlstn/sDcekQjyyf3OBAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technology Jamshedpur","correspondingAuthor":true,"prefix":"","firstName":"Rajesh","middleName":"","lastName":"Kumar","suffix":""},{"id":344699017,"identity":"5fbad87f-ad96-4132-b932-8ba6deaf3fb2","order_by":1,"name":"Ananyo Bhattacharya","email":"","orcid":"","institution":"National Institute of Technology Jamshedpur","correspondingAuthor":false,"prefix":"","firstName":"Ananyo","middleName":"","lastName":"Bhattacharya","suffix":""},{"id":344699018,"identity":"d53a0597-b656-422f-ba1d-15f0c989a552","order_by":2,"name":"Aanchal Singh S. 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It emphasizes the widespread application of artificial intelligence (AI) in MPPT methods in solar power systems, which can significantly improve performance and efficiency. However, AI-based MPPT techniques may be more computationally intensive and costly. Hybrid MPPT methods combine traditional and AI techniques to balance performance and complexity, addressing these issues. Fuzzy logic control (FLC) emerges as a viable technique, though synchronization is required for optimal performance under variable irradiation and weather conditions. This paper uses MATLAB/Simulink to compare the four main MPPT techniques: fuzzy-based variable step size P\u0026amp;O, FLC, and hybrid P\u0026amp;O-FLC algorithms. Further, a symmetric fuzzy controller, the perturb-and-observe (P\u0026amp;O) method, and a mixed method that combines both fuzzy and P\u0026amp;O-based controllers were compared. At the same time, many other FLC-based MPPT strategies were also examined. 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