Revolutionizing photovoltaic power: An enhanced Grey Wolf Optimizer for ultra-efficient MPPT under partial shading conditions

IF 3.3 Q2 MULTIDISCIPLINARY SCIENCES Scientific African Pub Date : 2025-03-01 Epub Date: 2025-02-18 DOI:10.1016/j.sciaf.2025.e02586

Hajar Ahessab, Ahmed Gaga, Benachir EL Hadadi

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Abstract

This paper presents an Enhanced Grey Wolf Optimizer (E-GWO) algorithm for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems under partial shading conditions. The proposed E-GWO introduces a novel parameter minimization strategy for the convergence factor

ω

, enabling rapid and precise tracking of the global maximum power point (GMPP) without overshoot. Key improvements to the standard GWO framework enhance tracking accuracy, stability, and overall system performance.

The proposed MPPT approach is validated through extensive simulations and real-world experiments implemented on a dual-core DSP LAUNCHXL-F28379D using MATLAB/Simulink. Experimental results demonstrate that E-GWO reduces tracking time by up to 99.90% compared to traditional GWO methods while increasing dynamic tracking efficiency by over 9%. Furthermore, the E-GWO consistently outperforms conventional GWO variants and other swarm-based algorithms, ensuring superior power output in diverse shading scenarios.

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革新光伏发电：增强型灰狼优化器，可在部分遮阳条件下实现超高效MPPT

提出了一种用于部分遮阳条件下光伏系统最大功率点跟踪的增强型灰狼优化算法（E-GWO）。所提出的E-GWO引入了一种新的收敛因子ω参数最小化策略，能够快速精确地跟踪全局最大功率点（GMPP）而不会超调。对标准GWO框架的关键改进增强了跟踪准确性、稳定性和整体系统性能。通过MATLAB/Simulink在双核DSP LAUNCHXL-F28379D上进行的大量仿真和实际实验验证了所提出的MPPT方法。实验结果表明，与传统的GWO方法相比，E-GWO方法的跟踪时间缩短了99.90%，动态跟踪效率提高了9%以上。此外，E-GWO始终优于传统的GWO变体和其他基于群的算法，确保在不同的遮阳场景下优越的功率输出。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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