基于烟头优化算法的最优太阳能电池参数估计

Reem Y. Abdelghany, S. Kamel, Hamdy M. Sultan, Mohamed H. Hassan, L. Nasrat
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引用次数: 0

摘要

在提高光伏发电系统效率的过程中,最重要的问题之一是找到正确的光伏发电模型。光伏系统的I-V特性是一种非线性关系,为了优化和仿真光伏系统,光伏模型的最优参数的确定是至关重要的。因此,要获得最佳PV模型需要有效的优化器。本文采用一种新的生物启发算法——灰燕鸥优化算法(STOA)来获取各种类型太阳能电池的未知参数值。实验结果表明,与其他优化算法相比,所使用的算法可以获得更准确的估计参数值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Solar Cell Parameter Estimation Based on Sooty Tern Optimization Algorithm
One of the most important issues in improving of the efficiency of the photovoltaic system (PV) is finding the correct PV model. Determination of optimum parameters for PV models is vital to optimize and simulate PV systems based on the I-V characteristic, which is a nonlinear relationship Therefore, reaching the best PV model requires effective optimizers. This paper applies a new bio-inspired algorithm called Sooty Tern Optimization Algorithm (STOA) to obtain values of unknown parameters of various types of solar cells. The results of the experiment showed that, compared to other optimization algorithms, the used algorithm can obtain more accurate values of the estimated parameters.
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