基于分组教与学优化算法的光伏电池模型参数辨识

杨莎; 张耀; 徐胜; 廖子文; 李俊贤

doi:10.16513/j.2096-2185.DE.2207307

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分布式能源 ›› 2022, Vol. 7 ›› Issue (3) : 52-61. DOI: 10.16513/j.2096-2185.DE.2207307

应用技术

基于分组教与学优化算法的光伏电池模型参数辨识

杨莎 ¹ ,
张耀 ² ,
徐胜 ² ,
廖子文 ¹ ,
李俊贤 ¹

作者信息 +

Parameter Identification of Photovoltaic Cell Model Based on Grouping Teaching-Learning-Based Optimization Algorithm

Sha YANG ¹ ,
Yao ZHANG ² ,
Sheng XU ² ,
Ziwen LIAO ¹ ,
Junxian LI ¹

Author information +

文章历史 +

本文亮点

为了提高光伏发电系统建模过程中光伏电池的参数辨识精度，在基本的教与学优化算法(teaching-learning-based optimization, TLBO)的基础上，针对其存在易于出现精度不高且陷入局部最优等问题，提出了一种基于分组的教与学优化算法(grouping teaching-learning-based optimization, GTLBO)。GTLBO算法在教学阶段采用了分组教学的方式，并对教学因子进行了改进，将GTLBO算法应用于单二极管模型、双二极管模型和3个光伏组件模型的参数提取。实验结果表明，与其他优化算法相比，GTLBO算法在光伏模型参数提取方面更加准确可靠。其次，相对于基础的TLBO算法，GTLBO算法的收敛速度和辨识精度都得到了提高，具有一定的可行性和实用性。

HeighLight

In order to improve the parameter identification accuracy of photovoltaic cells in the process of photovoltaic power generation system modeling, based on the basic teaching-learning-based optimization (TLBO) algorithm, aiming at the problems of low accuracy and falling into local optimum, A grouping teaching-learning-based optimization (GTLBO) algorithm is proposed. In the teaching stage, the GTLBO algorithm adopts the grouping teaching method and improves the teaching factors. The GTLBO algorithm is applied to the parameter extraction of single diode model, double diode model and three photovoltaic module model. Experimental results show that compared with other optimization algorithms, GTLBO algorithm is more accurate and reliable in photovoltaic model parameter extraction. Secondly, compared with the basic TLBO algorithm, the convergence speed and identification accuracy of GTLBO algorithm are improved, and it has certain feasibility and practicability.

导出引用

杨莎, 张耀, 徐胜, 等. 基于分组教与学优化算法的光伏电池模型参数辨识[J]. 分布式能源. 2022, 7(3): 52-61 https://doi.org/10.16513/j.2096-2185.DE.2207307

Sha YANG, Yao ZHANG, Sheng XU, et al. Parameter Identification of Photovoltaic Cell Model Based on Grouping Teaching-Learning-Based Optimization Algorithm[J]. Distributed Energy Resources. 2022, 7(3): 52-61 https://doi.org/10.16513/j.2096-2185.DE.2207307

中图分类号： TK51; TM914.4

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