Recently, solar photovoltaic (PV) systems are becoming the tendency theme motivating researchers focus. The appropriate design of PV cells is an important task, challenged by the development of a useful model able to simulate the current vs voltage characteristics of the real solar cell and the accurate estimation of the PV cell’s parameter values. This paper proposes an improved Levy flight based grasshopper optimization algorithm (LGOA) to estimate the parameters of three PV models, i.e., single diode, double diode, and PV module. The incorporation of Levy flight trajectory to the basic grasshopper optimization algorithm (GOA), ensure solutions diversity and enhances the exploration and exploitation capabilities as well. To further validate its effectiveness LGOA is applied to the Sharp ND-R250A5 module under different operating conditions of irradiance and temperature. Experimental results demonstrate that LGOA has the ability to extract the parameters of PV models with high performance and good accuracy compared to the standard GOA.

中文翻译：

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使用基于增强征费飞行的蚱蜢优化算法的太阳能光伏模型参数提取

最近，太阳能光伏（PV）系统正成为推动研究人员关注的趋势主题。PV 电池的适当设计是一项重要任务，面临着开发能够模拟真实太阳能电池的电流与电压特性以及准确估计 PV 电池参数值的有用模型的挑战。本文提出了一种改进的基于Levy飞行的蚱蜢优化算法(LGOA)来估计三种光伏模型的参数，即单二极管、双二极管和光伏组件。将 Levy 飞行轨迹纳入基本的蚱蜢优化算法 (GOA)，确保解决方案的多样性，并增强探索和开发能力。为了进一步验证其有效性，将 LGOA 应用于夏普 ND-R250A5 模块在不同辐照度和温度的工作条件下。实验结果表明，与标准GOA相比，LGOA能够以高性能和良好的精度提取光伏模型的参数。