A new intelligent control and advanced global optimization methodology for peak solar energy system performance under challenging shading conditions

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dc.authorid0000-0002-4353-1261
dc.authorid0000-0002-4049-0716
dc.authorid0000-0001-6944-4775
dc.contributor.authorWei, Xiqing
dc.contributor.authorHarrison, Ambe
dc.contributor.authorNaser, Abdulbari Talib
dc.contributor.authorMbasso, Wulfran Fendzi
dc.contributor.authorDagal, Idriss
dc.contributor.authorAlombah, Njimboh Henry
dc.contributor.authorJangir, Pradeep
dc.date.accessioned2026-01-31T15:08:15Z
dc.date.available2026-01-31T15:08:15Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis paper addresses the pressing challenge of mitigating energy losses in photovoltaic (PV) systems caused by partial shading conditions (PSC), a critical barrier to achieving optimal solar energy efficiency and reliability. The study introduces a breakthrough Global Maximum Power Point Tracking (GMPPT) methodology, designed to navigate the intricate dynamics of complex shading scenarios, thereby offering a transformative approach to maximizing energy yield. The methodology is built around the Confident Neighborhood Identification Mechanism (CNIM), which operates on the hypothesis that identifying a confident neighborhood around the GMPP facilitates uninterrupted and precise tracking of the true GMPP. CNIM leverages a climatic sensorless neural network to compute distributed optimal points across individual modules in real time. A globalization algorithm consolidates these results to establish a reliable GMPP zone, ensuring 100 % confidence in accurate tracking. Further innovation is realized in the Finite Two-Stage Tracking (FTST) control algorithm, which combines rapid pre-acceleration of the operating point into the GMPP zone with fine-tuned adjustments for precision tracking, achieving convergence in as little as 18 milliseconds under dynamic shading conditions. Empirical evaluations conducted on over 200 shading patterns demonstrate the methodology's robustness, achieving 100 % GMPP identification confidence and an average tracking efficiency of 99.87 %, outperforming state-of-the-art metaheuristic algorithms, including particle swarm optimization (PSO), Grey Wolf Optimization (GWO), Salp Swarm Optimization (SSO), and Improved Differential Evolution (IDE). Unlike state-of-the-art approaches, the proposed system eliminates the reliance on expensive climatic sensors, using only electrical measurements, which enhances affordability and real-time applicability. The results underscore the relevance of this study in advancing the reliability of PV systems in diverse environmental conditions. By mitigating shading-induced energy losses and ensuring high tracking precision, this novel methodology marks a significant stride toward sustainable and efficient solar energy deployment, capable of meeting the demands of modern renewable energy systems.
dc.description.sponsorshipKing Saud University, Riyadh, Saudi Arabia [RSPD2025R704]
dc.description.sponsorshipThe authors extend their appreciation to King Saud University, Saudi Arabia, for funding this work through Researchers Supporting Project number (RSPD2025R704), King Saud University, Riyadh, Saudi Arabia.r Arabia, for funding this work through Researchers Supporting Project number (RSPD2025R704) , King Saud University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1016/j.apenergy.2025.125808
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.scopus2-s2.0-105002007195
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org./10.1016/j.apenergy.2025.125808
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10640
dc.identifier.volume390
dc.identifier.wosWOS:001469628600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofApplied Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260128
dc.subjectPhotovoltaic (PV) systems
dc.subjectPartial shading conditions (PSC)
dc.subjectGlobal maximum power point tracking (GMPPT)
dc.subjectConfident neighborhood identification mechanism (CNIM)
dc.subjectFinite two-stage tracking (FTST)
dc.subjectMetaheuristic algorithms
dc.titleA new intelligent control and advanced global optimization methodology for peak solar energy system performance under challenging shading conditions
dc.typeArticle

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