Ripple Minimization in Renewable Energy Integration Improvement: Hybrid Intelligent Non-Linear Controller for PV/Wind/SAPF

Ripple Minimization in Renewable Energy Integration Improvement: Hybrid Intelligent Non-Linear Controller for PV/Wind/SAPF | 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 Ripple Minimization in Renewable Energy Integration Improvement: Hybrid Intelligent Non-Linear Controller for PV/Wind/SAPF Mustapha Meraouah, Said Hassaine, Faiza Kaddari, Sandrine Moreau, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6436130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Electrical Engineering → Version 1 posted 8 You are reading this latest preprint version Abstract The emergence of renewable energy sources and the need to optimize power quality in the grid have led to the development of new, sophisticated control approaches. These methods enable systems to supply nonlinear loads and inject excess energy into the grid. Photovoltaic and wind energy are integrated via an active shunt power filter (SAPF). The system injects excess energy into the grid while reducing total harmonic distortion (THD) and minimizing ripple in renewable energy production. This paper proposes hybrid techniques for maximum power point tracking (MPPT) for the PV and wind systems, which include the use of intelligent adaptive super sliding mode control (STSMC) based on deep artificial neural network (ANN) hybridization, a modified optimal relation based on model predictive control for the switching generation (ORB-MPC), and SAPF strategy control based on direct power with model predictive control (DPMPC). The results of the comparative study between conventional and proposed control approaches demonstrate significant superiority in maximizing renewable energy production, achieving efficiencies of 99.9% for PV systems and 99.3% for wind systems, with low fluctuations and minimized total harmonic distortion in the grid. This leads to rapid convergence of the DC link voltage, with settling times reduced by more than 20% compared to conventional approaches.. Hybrid Adaptive ANN-STSMC controller PV Wind SAPF Total Harmonics Distortion Power Quality Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Electrical Engineering → Version 1 posted Reviews received at journal 19 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 18 May, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 14 Apr, 2025 First submitted to journal 12 Apr, 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. 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-6436130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458629204,"identity":"761b9f59-4c6f-4db3-b0d2-e8d484b42544","order_by":0,"name":"Mustapha 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