Parameter identification of solar cells using artificial bee colony optimization
Parameter identification of solar cells using artificial bee colony optimization
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DOI：
10.1016/j.energy.2014.05.011
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发表时间：
2014-08
期刊：
影响因子：
9
通讯作者：
Diego Oliva;Erik Cuevas;G. Pajares
中科院分区：
文献类型：
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作者：
Diego Oliva;Erik Cuevas;G. Pajares
为了提高太阳能系统的性能，太阳能电池的电流-电压（I-V）特性的精确建模引起了各种研究的关注。精确建模的主要缺点是缺乏关于确实表征太阳能电池的精确参数值的信息。由于这样的参数不能提取的太阳能电池的规格，优化技术是必要的，以调整实验数据的太阳能电池模型。考虑到太阳能电池的I-V特性，优化任务涉及到复杂的非线性和多模态目标函数的求解。已经提出了几种优化方法来识别太阳能电池的参数。然而，他们中的大多数获得次优解，由于其过早收敛和难以克服局部极小值的多模态问题。提出了利用人工蜂群算法对太阳能电池参数进行精确辨识的方法。ABC算法是受蜜蜂智能觅食行为的启发而提出的一种进化方法。与其他进化算法相比，ABC算法具有更好的搜索能力，面对多模态的目标函数。为了说明所提出的方法的熟练程度，它是比较其他知名的优化方法。实验结果表明，所提出的方法在鲁棒性和准确性方面的高性能。
In order to improve the performance of solar energy systems, accurate modeling of current vs. voltage (I–V) characteristics of solar cells has attracted the attention of various researches. The main drawback in accurate modeling is the lack of information about the precise parameter values which indeed characterize the solar cell. Since such parameters cannot be extracted from the datasheet specifications, an optimization technique is necessary to adjust experimental data to the solar cell model. Considering theI–Vcharacteristics of solar cells, the optimization task involves the solution of complex non-linear and multi-modal objective functions. Several optimization approaches have been proposed to identify the parameters of solar cells. However, most of them obtain sub-optimal solutions due to their premature convergence and their difficulty to overcome local minima in multi-modal problems. This paper proposes the use of the ABC (artificial bee colony) algorithm to accurately identify the solar cells' parameters. The ABC algorithm is an evolutionary method inspired by the intelligent foraging behavior of honey bees. In comparison with other evolutionary algorithms, ABC exhibits a better search capacity to face multi-modal objective functions. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known optimization methods. Experimental results demonstrate the high performance of the proposed method in terms of robustness and accuracy.