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<p>In this paper, numerous optimization techniques for Maximum Power Point Tracking in solar photovoltaic systems are appraised. Four advanced algorithms are the subject of this study: Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Genetic Algorithm (GA). The effectiveness of each method is assessed according to how well it can monitor the Maximum power point in a given amount of time and accuracy under various environmental factors like temperature and irradiance. This comparative study simulates and analyzes convergence speed, efficiency, stability, and computational complexity. The findings show that although all four algorithms successfully improve MPPT efficiency in solar PV systems, their convergence behavior and processing requirements differ. The GA has higher computational costs but is robust under a variety of situations. PSO exhibits quick convergence, although in complicated situations, it could have local optima. Effective in dynamic contexts, CSA provides a strong mix between exploration and exploitation capabilities and GWO has good accuracy and tracking speed.</p>

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Performance analysis of optimization MPPT used in solar photo-voltaic system

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Jayanta Kumar Sahu,
  • G. N. Mamatha,
  • Barsa Samantaray,
  • S. K. Sunori,
  • Raj Kumar,
  • P. Sasi Kiran,
  • Apurav Gautam,
  • Jashandeep Singh

摘要

In this paper, numerous optimization techniques for Maximum Power Point Tracking in solar photovoltaic systems are appraised. Four advanced algorithms are the subject of this study: Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Genetic Algorithm (GA). The effectiveness of each method is assessed according to how well it can monitor the Maximum power point in a given amount of time and accuracy under various environmental factors like temperature and irradiance. This comparative study simulates and analyzes convergence speed, efficiency, stability, and computational complexity. The findings show that although all four algorithms successfully improve MPPT efficiency in solar PV systems, their convergence behavior and processing requirements differ. The GA has higher computational costs but is robust under a variety of situations. PSO exhibits quick convergence, although in complicated situations, it could have local optima. Effective in dynamic contexts, CSA provides a strong mix between exploration and exploitation capabilities and GWO has good accuracy and tracking speed.