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Laplacian Nelder-Mead spherical evolution for parameter estimation of photovoltaic models

机译：Laplacian Nelder-Mead球形演进，用于光伏模型的参数估计

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摘要

A critical aspect of the design, simulation, control, and optimization of Photovoltaic (PV) systems is evaluating the PV model's optimal parameter values based on the actual measured voltage and current values. To address that concern, an enhanced spherical evolution algorithm with no metaphor is proposed for identifying unknown parameters in PV models. The algorithm combines Laplace's cross search mechanism (LCS) and the Nelder-Mead simplex method (NMs), called LCNMSE. In a sense, the goal of LCS is to enrich the diversity of solution sets and make the variety of solutions coarser. The NMs enhances the algorithm exploitation by further scanning more promising ranges in the local region. This idea is developed to improve the local optimal solution's accuracy. In conjunction with both, a balance between exploration and exploitation is maintained. To verify the effectiveness of LCNMSE on high and multi-peaks cases, it is compared with eight state-of-the-art and basic algorithms based on 28 benchmark functions selected from 23 benchmark functions and 30 IEEE CEC2014 benchmark problems. Then, the method is utilized to evaluate the solar cells' parameters and PV modules. Experiments show that the algorithm performs well in evaluating different PV models' unknown parameters than other existing algorithms. Therefore, LCNMSE is an accurate and efficient technique for solar cell and PV models' parameter extraction problems. For further info or any question on metaphor-free LCNMSE, please visit https://aliasgharheidari.com.
机译：光伏（PV）系统的设计，仿真，控制和优化的关键方面是基于实际测量的电压和电流值来评估PV模型的最佳参数值。为了解决这个问题，提出了一种没有隐喻的增强的球形演进算法，用于识别PV模型中的未知参数。该算法结合了LAPLACE的交叉搜索机制（LCS）和NELDER-MED SIMPLEX方法（NMS），称为LCNMSE。从某种意义上说，LCS的目标是丰富解决方案集的多样性，并使各种解决方案造创。 NMS通过进一步扫描当地区域中的更有前途的范围来增强算法利用。开发了这种想法，以提高当地最佳解决方案的准确性。与两者均一样，维持勘探和剥削之间的平衡。为了验证LCNMSE对高峰和多峰的有效性，它与八个最先进的和基本算法进行比较，基于28个基准函数和30个IEEE CEC2014基准问题。然后，利用该方法来评估太阳能电池的参数和PV模块。实验表明，该算法在评估不同PV模型的未知参数时表现良好，而不是其他现有算法。因此，LCNMSE是太阳能电池和光伏模型的参数提取问题的准确有效的技术。有关更多信息或无隐喻LCNMSE的任何问题，请访问https://aliasgharheidar.com。

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