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<p>Modelling the circuit model parameters of photovoltaic (PV) cells and modules is one of the significant encounters in the field of solar energy. Lately, with the advance of the application of optimization algorithms, approximating the PV module parameters can be changed into an optimization problem. This research offers an optimization pipeline for the optimal collection of the parameters in the PV systems. The method is founded on a novel combination of a metaheuristic algorithm, termed AHEO (Adapted Human Evolutionary Optimizer) for the current goal. The key purpose of employing the AHEO in the paper is to minimalize the root mean square error (RMSE) between the forecast and the measured I–V curves of the PV system. The method has been confirmed on a commercial PV module and the results show its high accuracy with a RMSE decrease of 34.6% related to the conventional optimization methods.</p>

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Parameter identification of the PV systems based on an adapted version of human evolutionary optimizer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月2日

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  • Jun Qian,
  • Hui Zhang,
  • Shun Wang

摘要

Modelling the circuit model parameters of photovoltaic (PV) cells and modules is one of the significant encounters in the field of solar energy. Lately, with the advance of the application of optimization algorithms, approximating the PV module parameters can be changed into an optimization problem. This research offers an optimization pipeline for the optimal collection of the parameters in the PV systems. The method is founded on a novel combination of a metaheuristic algorithm, termed AHEO (Adapted Human Evolutionary Optimizer) for the current goal. The key purpose of employing the AHEO in the paper is to minimalize the root mean square error (RMSE) between the forecast and the measured I–V curves of the PV system. The method has been confirmed on a commercial PV module and the results show its high accuracy with a RMSE decrease of 34.6% related to the conventional optimization methods.