基于分解的自适应差分进化识别光伏模型参数

doi:10.3969/j.issn.0255-8297.2022.05.001

Abstract

Abstract: In order to quickly, accurately and reliably identify the parameters of photovoltaic (PV) models under different environmental conditions, an improved adaptive differential evolution algorithm based on improved adaptive differential evolution with decomposition (IADE-D) is proposed. In IADE-D, first, an unknown parameter decomposition technique is proposed to reduce the dimension of a problem and thus reduce the complexity of the problem. Second, an improved adaptive differential evolution algorithm is employed to solve the decomposed unknown parameters. In order to verify the effectiveness of the proposed algorithm, it is used for the single diode-based PV panel model parameter identification, namely, multi-crystalline KC200GT. Simulation results show that the IADE-D algorithm proposed in this paper is more competitive in terms of the accuracy and reliability than some of the advanced algorithms proposed recently. Therefore, IADE-D can be considered as an effective method for parameter identification of PV models.

Key words: photovoltaic models, parameter identification, differential evolution, adaptive, decomposition

CLC Number:

TP39

YAN Zhen, LI Shuijia, GONG Wenyin. Parameter Identification of Photovoltaic Models Based on Adaptive Differential Evolution with Decomposition[J]. Journal of Applied Sciences, 2022, 40(5): 713-726.

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Parameter Identification of Photovoltaic Models Based on Adaptive Differential Evolution with Decomposition

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