基于改进蝗虫优化算法的光伏电池模型参数辨识

doi:10.3969/j.issn.1000-1158.2020.12.15

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Abstract To overcome the shortcomings of grasshopper optimization algorithm, such as easily falling into local optimum and insufficient convergence accuracy, an improved grasshopper optimization algorithm (IGOA) is proposed. Combing grasshopper optimization algorithm with chaotic algorithm, chaotic initialization is carried out to improve the quality of initial population. Then the differential strategy of differential evolution algorithm is introduced to maintain the diversity of the population through the process of mutation, crossover and selection, and increase the possibility of the algorithm jumping out of the local optimum, so that the algorithm can search for better solutions. The method of particle swarm optimization is introduced in the part of position updating. To accelerate the convergence speed of the algorithm, the current particle optimum value is used as the target. IGOA is used to identify the parameters of polycrystalline silicon solar cells. Compared with other intelligent optimization algorithms, the effectiveness and superiority of IGOA are verified. The effectiveness of IGOA to identify parameters of solar cell under different illumination is also verified by experiments.

Key words： Key words: metrology photovoltaic cell parameter identification improved grasshopper optimization algorithm differential evolution algorithm chaotic initialization

Received: 13 February 2019 Published: 08 December 2020

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TB971

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	WU Zhong-qiang
	SHEN Dan-dan
	SHANG Meng-yao
	QI Song-qi

Cite this article:

WU Zhong-qiang,SHEN Dan-dan,SHANG Meng-yao, et al. Parameter Identification of Photovoltaic Cell Model Based on Improved Grasshopper Optimization Algorithm[J]. Acta Metrologica Sinica, 2020, 41(12): 1536-1543.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.12.15 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I12/1536

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