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<p>Metaheuristic global maximum power point tracking (GMPPT) methods often suffer from prolonged convergence, power oscillations, and reduced tracking efficiency due to unnecessary exploration of low-power regions under partial shading conditions (PSC). To address these limitations, this paper proposes a GMPPT framework for photovoltaic (PV) systems that combines the parameter-free JAYA algorithm with two search-space reduction mechanisms: Voltage Interval Reduction (VIR) and Local Maximum Power Point Identification and Skipping (LMPPIS). Unlike typical metaheuristic approaches that explore the entire voltage range, the proposed method dynamically confines the search to voltage-bounded intervals containing potential MPPs through VIR mechanism and progressively eliminates intervals associated with local maxima using LMPPIS mechanism. By focusing on promising regions, the method accelerates convergence and reduces power losses. The proposed framework is validated through extensive simulation and experimental studies under diverse and challenging PSC patterns. Results demonstrate the best-case performance achieved 97.19% tracking efficiency with a tracking time of 1.1&#xa0;s, respectively. Furthermore, the proposed method obtained average tracking efficiency above 96% and average tracking times of 1.25&#xa0;s in simulation and 1.35&#xa0;s in experiments, outperforming IPSO, IGWO, and conventional JAYA by over ~ 50% in tracking time and more than 1% in tracking efficiency.</p>

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Efficient global MPPT for PV systems via search-space reduction and JAYA optimization under diverse partial shading conditions

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Dhia Elhak Rouabah,
  • Maaspaliza Azri,
  • Mohd Zulkifli Ramli,
  • Abdelhak Lekbir

摘要

Metaheuristic global maximum power point tracking (GMPPT) methods often suffer from prolonged convergence, power oscillations, and reduced tracking efficiency due to unnecessary exploration of low-power regions under partial shading conditions (PSC). To address these limitations, this paper proposes a GMPPT framework for photovoltaic (PV) systems that combines the parameter-free JAYA algorithm with two search-space reduction mechanisms: Voltage Interval Reduction (VIR) and Local Maximum Power Point Identification and Skipping (LMPPIS). Unlike typical metaheuristic approaches that explore the entire voltage range, the proposed method dynamically confines the search to voltage-bounded intervals containing potential MPPs through VIR mechanism and progressively eliminates intervals associated with local maxima using LMPPIS mechanism. By focusing on promising regions, the method accelerates convergence and reduces power losses. The proposed framework is validated through extensive simulation and experimental studies under diverse and challenging PSC patterns. Results demonstrate the best-case performance achieved 97.19% tracking efficiency with a tracking time of 1.1 s, respectively. Furthermore, the proposed method obtained average tracking efficiency above 96% and average tracking times of 1.25 s in simulation and 1.35 s in experiments, outperforming IPSO, IGWO, and conventional JAYA by over ~ 50% in tracking time and more than 1% in tracking efficiency.