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

Xiqing Wei, Ambe Harrison, Abdulbari Talib Naser, Wulfran Fendzi Mbasso, Idriss Dagal, Njimboh Henry Alombah, Pradeep Jangir, Mohamed Sharaf and Mohammed El-Meligy

Applied Energy, 2025, vol. 390, issue C, No S0306261925005380

Abstract: This 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.

Keywords: Photovoltaic (PV) systems; Partial shading conditions (PSC); Global maximum power point tracking (GMPPT); Confident neighborhood identification mechanism (CNIM); Finite two-stage tracking (FTST); Metaheuristic algorithms (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125808

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