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<p>This paper presents a robust MPPT control strategy for photovoltaic (PV) systems under real climatic conditions. The proposed method integrates Backstepping Sliding Mode Control (BSMC) with the Bald Eagles Search (BES) optimization algorithm to enhance tracking performance and system robustness. By combining the adaptability of BSMC with the optimization capability of BES, the approach ensures precise and stable convergence to the Maximum Power Point (MPP). Lyapunov-based analysis confirms system stability, while simulations using both synthetic and real meteorological data from Errachidia, Morocco, validate the method’s effectiveness. The BES-BSMC strategy achieves superior performance, with a steady-state error of 6 × 10⁻<sup>4</sup>&#xa0;V, response time of 1.72&#xa0;s, overshoot of 1.14%, chattering magnitude of 4.5 × 10⁻⁷ V, and efficiency of 99.53%. These results clearly outperform conventional SMC and ISMC controllers. The study demonstrates the benefits of integrating intelligent optimization with nonlinear control for reliable solar energy harvesting under dynamic environmental conditions.</p>

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Bald Eagle Search Optimization Based MPPT Control Strategy for a Stand-Alone PV System Operating Under Real Climatic Conditions

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • I. Bekki,
  • H. Rizki,
  • F. Lamzouri,
  • A. El Amrani,
  • E. Boufounas

摘要

This paper presents a robust MPPT control strategy for photovoltaic (PV) systems under real climatic conditions. The proposed method integrates Backstepping Sliding Mode Control (BSMC) with the Bald Eagles Search (BES) optimization algorithm to enhance tracking performance and system robustness. By combining the adaptability of BSMC with the optimization capability of BES, the approach ensures precise and stable convergence to the Maximum Power Point (MPP). Lyapunov-based analysis confirms system stability, while simulations using both synthetic and real meteorological data from Errachidia, Morocco, validate the method’s effectiveness. The BES-BSMC strategy achieves superior performance, with a steady-state error of 6 × 10⁻4 V, response time of 1.72 s, overshoot of 1.14%, chattering magnitude of 4.5 × 10⁻⁷ V, and efficiency of 99.53%. These results clearly outperform conventional SMC and ISMC controllers. The study demonstrates the benefits of integrating intelligent optimization with nonlinear control for reliable solar energy harvesting under dynamic environmental conditions.