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<p>In photovoltaic systems, PSC occur when PV panels are exposed to nonuniform solar irradiance levels. Extracting the maximum power from PV systems operating under PSC represents a complex and challenging task for MPPT algorithms. Optimization-based MPPT techniques have therefore gained significant attention due to their ability to achieve fast convergence and high efficiency under such conditions. In this study, a novel M-DHO algorithm is proposed by integrating the DHO algorithm, which is inspired by the cooperative hunting behavior of the Asiatic wild dog, with a Levy flight strategy to enhance global search capability. Especially with Levy flight support, the M-DHO algorithm eliminates the problems of fast convergence and getting stuck in a local minimum. Furthermore, while the fast convergence problem is eliminated, the Levy Flight algorithm allows reaching the global maximum value faster with high accuracy in complex optimization problems. Nine distinct PSC scenarios are created across six different voltage regions, and the performance of the proposed M-DHO algorithm is comparatively assessed against GWO, WOA, FPA, and the conventional DHO algorithm. Simulation results demonstrate that the proposed M-DHO algorithm achieves faster convergence to the global maximum power point and higher tracking efficiency compared to the benchmark algorithms. When averaged over all scenarios, M-DHO achieved an average extracted power of 838.58W, tracking speed of 0.15s and an average tracking efficiency of 99.52%, outperforming other algorithms.</p>

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Modified Dhole-inspired optimization for maximum power extraction in photovoltaic systems under partial shading

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Resat Celikel,
  • Omur Aydogmus,
  • Musa Yilmaz

摘要

In photovoltaic systems, PSC occur when PV panels are exposed to nonuniform solar irradiance levels. Extracting the maximum power from PV systems operating under PSC represents a complex and challenging task for MPPT algorithms. Optimization-based MPPT techniques have therefore gained significant attention due to their ability to achieve fast convergence and high efficiency under such conditions. In this study, a novel M-DHO algorithm is proposed by integrating the DHO algorithm, which is inspired by the cooperative hunting behavior of the Asiatic wild dog, with a Levy flight strategy to enhance global search capability. Especially with Levy flight support, the M-DHO algorithm eliminates the problems of fast convergence and getting stuck in a local minimum. Furthermore, while the fast convergence problem is eliminated, the Levy Flight algorithm allows reaching the global maximum value faster with high accuracy in complex optimization problems. Nine distinct PSC scenarios are created across six different voltage regions, and the performance of the proposed M-DHO algorithm is comparatively assessed against GWO, WOA, FPA, and the conventional DHO algorithm. Simulation results demonstrate that the proposed M-DHO algorithm achieves faster convergence to the global maximum power point and higher tracking efficiency compared to the benchmark algorithms. When averaged over all scenarios, M-DHO achieved an average extracted power of 838.58W, tracking speed of 0.15s and an average tracking efficiency of 99.52%, outperforming other algorithms.