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<p>Photovoltaic systems are the most mature and promising technology for the generation of clean electricity. However, due to their non-convex, nonlinear, and multi-parametric characteristics, models and methods must be developed for optimizing their operation in different environments and conditions for forecasting power and determining the efficiency of a PV plant. Several algorithms have aimed at accurately defining the parameters and most of them focus on improving both exploration and exploitation of the method by combining different techniques. On the other hand, a simple change in a model can improve the accuracy of parameters. This study proposes a small alternation in the model, i.e., inclusion of synchronization inputs by master-slave coupling between real and mathematical systems. The coupling was used with swarm mean-variance mapping optimization (Swarm MVMO) metaheuristic method and applied to a photovoltaic module model (PVM) for different temperature and irradiance measurements. The results showed an improvement in the estimated parameters after the aforementioned inclusion in comparison with another method from the literature.</p>

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Master-Slave Synchronization for Parameter Estimation of Photovoltaic Module Model

  • 期刊论文

  • 发表日期：

    2025年5月31日

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  • Gustavo Henrique de Paula Santos,
  • Bader Sager,
  • Fekadu Shewarega,
  • Hendrik Vennegeerts,
  • Moisés Carlos Tanca Villanueva,
  • Elmer Pablo Tito Cari

摘要

Photovoltaic systems are the most mature and promising technology for the generation of clean electricity. However, due to their non-convex, nonlinear, and multi-parametric characteristics, models and methods must be developed for optimizing their operation in different environments and conditions for forecasting power and determining the efficiency of a PV plant. Several algorithms have aimed at accurately defining the parameters and most of them focus on improving both exploration and exploitation of the method by combining different techniques. On the other hand, a simple change in a model can improve the accuracy of parameters. This study proposes a small alternation in the model, i.e., inclusion of synchronization inputs by master-slave coupling between real and mathematical systems. The coupling was used with swarm mean-variance mapping optimization (Swarm MVMO) metaheuristic method and applied to a photovoltaic module model (PVM) for different temperature and irradiance measurements. The results showed an improvement in the estimated parameters after the aforementioned inclusion in comparison with another method from the literature.