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<p>The power-voltage characteristics of photovoltaic arrays are highly complex, often exhibiting multiple peaks under partial shading conditions. This complexity highlights the need for the development of effective optimization algorithms. The key challenge lies in identifying the Global Maximum Power Point, rather than settling for the Local Maximum Power Point. In recent years, swarm-based algorithms, particularly those utilizing adaptive swarm size strategies, have gained prominence. These approaches, are distinguished by dynamic population sizes and the ability to adjust the number of agents within sub-swarms, thereby enhancing both search efficiency and robustness. This study introduces a novel Maximum Power Point Tracking algorithm that employs the Zebra Optimization Algorithm to improve the power generation efficiency of photovoltaic systems operating under partial shading conditions. The Zebra Optimization, inspired by the natural behavior of gregarious animals that live in herds, is a metaheuristic optimization method. It is integrated with the photovoltaic system's Maximum Power Point Tracking controller to effectively track the Global Maximum Power Point under partial shading conditions. The research utilizes an Algerian photovoltaic system model, with experimental data collected over two days to represent both clear and cloudy conditions. The system consists of multiple arrays, each comprising of two strings of 15 photovoltaic modules connected in series. A comparative analysis is then conducted with state-of-the-art Maximum Power Point Tracking techniques, including Grey Wolf Optimization, Particle Swarm Optimization, and Artificial Bee Colony. The proposed Zebra Optimization Algorithm-based Maximum Power Point Tracking is tested through both simulations and real-world experiments to evaluate its performance. The results demonstrate excellent tracking efficiency, rapid convergence, robustness, and ease of implementation. The algorithm performs effectively across a wide range of partial shading conditions and weather conditions, showcasing its potential for the development of efficient and reliable Maximum Power Point Tracking algorithms for photovoltaic systems, especially in challenging partial shading scenarios.</p>

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Optimization of solar photovoltaic maximum power point tracking via an enhanced zebra algorithm accounting for multiple operating conditions

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Elmamoune Halassa,
  • Seghiou Abdellatif,
  • Lakhdar Mazouz,
  • Mostefaoui Imene Meriem,
  • Aissa Chouder,
  • Abdlhamid Rabhi

摘要

The power-voltage characteristics of photovoltaic arrays are highly complex, often exhibiting multiple peaks under partial shading conditions. This complexity highlights the need for the development of effective optimization algorithms. The key challenge lies in identifying the Global Maximum Power Point, rather than settling for the Local Maximum Power Point. In recent years, swarm-based algorithms, particularly those utilizing adaptive swarm size strategies, have gained prominence. These approaches, are distinguished by dynamic population sizes and the ability to adjust the number of agents within sub-swarms, thereby enhancing both search efficiency and robustness. This study introduces a novel Maximum Power Point Tracking algorithm that employs the Zebra Optimization Algorithm to improve the power generation efficiency of photovoltaic systems operating under partial shading conditions. The Zebra Optimization, inspired by the natural behavior of gregarious animals that live in herds, is a metaheuristic optimization method. It is integrated with the photovoltaic system's Maximum Power Point Tracking controller to effectively track the Global Maximum Power Point under partial shading conditions. The research utilizes an Algerian photovoltaic system model, with experimental data collected over two days to represent both clear and cloudy conditions. The system consists of multiple arrays, each comprising of two strings of 15 photovoltaic modules connected in series. A comparative analysis is then conducted with state-of-the-art Maximum Power Point Tracking techniques, including Grey Wolf Optimization, Particle Swarm Optimization, and Artificial Bee Colony. The proposed Zebra Optimization Algorithm-based Maximum Power Point Tracking is tested through both simulations and real-world experiments to evaluate its performance. The results demonstrate excellent tracking efficiency, rapid convergence, robustness, and ease of implementation. The algorithm performs effectively across a wide range of partial shading conditions and weather conditions, showcasing its potential for the development of efficient and reliable Maximum Power Point Tracking algorithms for photovoltaic systems, especially in challenging partial shading scenarios.