基于自适应差分演化算法的光伏模型参数提取-《郑州大学学报(工学版)》

文章信息/Info

Title:: Parameter Extraction of Photovoltaic Models Based on Adaptive Differential Evolution Algorithm

Author(s):: Li ShuijiaGong Wenxi; School of Computer Science, China University of Geosciences (Wuhan).

摘要:: 快速准确地提取光伏（PV）模型的参数对于光伏系统的模拟，评估和控制是至关重要的。近些年来，使用智能优化方法对PV模型进行参数提取得到了极大的关注。然而，这些智能优化方法往往消耗了大量的计算资源。为了准确而快速的提取光伏模型的参数，本文提出了一种新型的自适应差分演化算法。在该算法中，提出了一种新的突变策略。为了验证算法的性能，选择单二极管模型，双二极管模型和PV模型作为测试模型。实验结果表明，提出的算法可以快速准确地提取到不同PV模型的参数。因此，提出的算法可以作为一种有效的PV模型参数提取的方法。

Abstract:: It is vital to fast and accurately extract the parameters of the photovoltaic (PV) models for the simulation, evaluation, and control of PV systems. Recently, the use of the intelligent optimization methods for parameter extraction of PV models draws growing attention. However, these methods tend to consum e large computational resources. In order to fast and accurately extract the parameters of the PV models, this paper develops a novel adaptive differential evolution algorithm, in which a new m utation strategy is proposed. To verify the performance of proposed algorithm, the single diode model, the double diode model, and the PV module are selected as the test models. The experimental results show that proposed algorithm can extract the parameters of different PV models fast and accurately. Thus, proposed algorithm can be an efficient alternative for parameter extract ion of PV models