为了快速、准确和可靠地识别不同环境条件下光伏模型参数，提出了一种基于分解的改进自适应差分进化（improved adaptive differential evolution with decomposition，IADE-D）算法。在IADE-D中，首先提出了一种未知参数分解技术来降低问题的维度，减少问题的复杂性。然后提出一种改进自适应差分进化算法用于求解分解后的未知参数。为了验证所提算法的有效性，将其用于一种基于单二极管的光伏面板模型参数识别。仿真结果表明，与现有先进算法相比，IADE-D算法在准确性和可靠性上更具有竞争力。因此，可以考虑将IADE-D作为一种有效的光伏模型参数识别方法。
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.
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