利用改进的白鲨优化算法对太阳能光伏电池/组件数学模型进行优化参数表征

M. Lakshmanan, C. Kumar, John Sahayam Jasper
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引用次数: 1

摘要

在光伏系统的建模和设计中,光伏电池/组件模型的参数表征一直是一个重要的研究领域。基于二极管的模型,如单二极管模型(SDM)、双二极管模型(DDM)和三二极管模型,经常被采用,其中SDM和DDM是最重要的模型。因此,通过使用目标函数来求解这些模型的参数表征,可以最小化估计电流与实验电流之间的差异。元启发式优化算法最近被用来解决快速找到准确和高可靠结果的困难。因此,本研究对基本SDM和DDM进行了修正,并考虑了基于修正模型的目标函数。此外,通过修改白鲨优化器(White Shark Optimizer, WSO)的力控制参数,提出了一种新的元启发式算法的改进版本,并注入混沌发生器以提高白鲨优化器的利用能力。将改进后的算法命名为IWSO,并应用于PV参数的提取。本文利用新的目标函数对传统PV模型和修正PV模型进行了比较。实验结果证明了IWSO在竞争算法中的优势。所提出的IWSO算法的平均弗里德曼秩检验值为1.171,优于所有选择的算法。改进的SDM和DDM模型的平均精度比传统PV模型提高了12%。结果表明,IWSO估计的参数值是最好的,估计电流与实验电流的差异最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm
In the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode‐based models, such as single‐diode model (SDM), double‐diode model (DDM), and the three‐diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current.
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