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Parameter extraction of solar photovoltaic models via quadratic interpolation learning differential evolution
Sustainable Energy & Fuels ( IF 5.6 ) Pub Date : 2020-08-28 , DOI: 10.1039/d0se01000f
Guojiang Xiong 1, 2, 3, 4, 5 , Jing Zhang 1, 2, 3, 4, 5 , Dongyuan Shi 5, 6, 7, 8 , Lin Zhu 9, 10, 11, 12 , Xufeng Yuan 1, 2, 3, 4, 5
Affiliation  

The parameter extraction problem of solar photovoltaic (PV) models is a highly nonlinear multimodal optimization problem. In this paper, quadratic interpolation learning differential evolution (QILDE) is proposed to solve it. Differential evolution (DE) is a preeminent metaheuristic algorithm with good exploration. However, its exploitation is poor, resulting in low searching precision when applied to the problem. To overcome this deficiency, in QILDE, quadratic interpolation (QI) is embedded in the crossover operation of DE to construct a QI learning-backup crossover operation to enhance the performance of DE. The mutation scheme of DE is primarily responsible for exploring the new search space while QI is mainly in charge of exploiting the local solution space around the best individual, which, therefore, can achieve a good trade-off between exploitation and exploration. QILDE is applied to six different PV cases. The experimental results demonstrate that QI coupled with the mutation scheme DE/best/2 can obtain superior results in solving the parameter extraction problem of PV models. Besides, compared with other advanced algorithms, QILDE shows highly competitive performance in terms of solution quality, extraction accuracy, robust stability, convergence property, computational time, and statistical significance. In addition, the current–voltage characteristics provided by QILDE agree well with the measured data for different PV models under different operating conditions.

中文翻译:

二次插值学习差分演化的太阳能光伏模型参数提取

太阳能光伏(PV)模型的参数提取问题是一个高度非线性的多峰优化问题。本文提出了二次插值学习差分进化算法(QILDE)来解决这个问题。差分进化(DE)是一种出色的元启发式算法,具有很好的探索性。但是,其开发性很差,应用于该问题时导致搜索精度低。为了克服这一缺陷,在QILDE中,将二次插值(QI)嵌入到DE的交叉操作中,以构造QI学习-备份交叉操作以增强DE的性能。DE的变异方案主要负责探索新的搜索空间,而QI主要负责开发围绕最佳个体的局部解空间,因此,可以在开发和勘探之间取得良好的折衷。QILDE适用于六个不同的PV情况。实验结果表明,QI与突变方案DE / best / 2结合可以解决PV模型的参数提取问题。此外,与其他高级算法相比,QILDE在解决方案质量,提取精度,鲁棒稳定性,收敛性,计算时间和统计意义方面均表现出极强的竞争力。此外,QILDE提供的电流-电压特性与在不同工作条件下不同PV型号的测量数据非常吻合。实验结果表明,QI与突变方案DE / best / 2结合可以解决PV模型的参数提取问题。此外,与其他高级算法相比,QILDE在解决方案质量,提取精度,鲁棒稳定性,收敛性,计算时间和统计意义方面均表现出极强的竞争力。此外,QILDE提供的电流-电压特性与在不同工作条件下不同PV型号的测量数据非常吻合。实验结果表明,QI与突变方案DE / best / 2结合可以解决PV模型的参数提取问题。此外,与其他高级算法相比,QILDE在解决方案质量,提取精度,鲁棒稳定性,收敛性,计算时间和统计意义方面均表现出极强的竞争力。此外,QILDE提供的电流-电压特性与在不同工作条件下不同PV型号的测量数据非常吻合。和统计意义。此外,QILDE提供的电流-电压特性与在不同工作条件下不同PV型号的测量数据非常吻合。和统计意义。此外,QILDE提供的电流-电压特性与在不同工作条件下不同PV型号的测量数据非常吻合。
更新日期:2020-09-15
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