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

Sha YANG; Yao ZHANG; Sheng XU; Ziwen LIAO; Junxian LI

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

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Distributed Energy ›› 2022, Vol. 7 ›› Issue (3) : 52-61. DOI: 10.16513/j.2096-2185.DE.2207307

Application Technology

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

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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.

Key words

teaching-learning-based optimization (TLBO) algorithm / photovoltaic cell model / parameter identification / group teaching / simulation analysis

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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

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