PARAMETER IDENTIFICATION OF SOLAR CELL BASED ON DATA DENOISING META-HEURISTIC ALGORITHM

Gu Minqi; Lyu Zhilin; Lu Jianfeng; Hai Tao; Wang Jun

doi:10.19912/j.0254-0096.tynxb.2024-0357

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 589-597. DOI: 10.19912/j.0254-0096.tynxb.2024-0357

Special Topics of Academic Papers at the 90th Annual Meeting of the China Association for Science and Technology

PARAMETER IDENTIFICATION OF SOLAR CELL BASED ON DATA DENOISING META-HEURISTIC ALGORITHM

Gu Minqi¹, Lyu Zhilin¹, Lu Jianfeng², Hai Tao¹, Wang Jun³

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Abstract

Noise in the measured voltage-current （V-I） data reduces the accuracy of solar cell parameter identification of heuristic algorithm （MhA）. To address this issue, a kind of data denoising meta-heuristic algorithm （DDMhA）, whose function is to precisely identify solar cell parameters, is proposed on the basis of kernel extreme learning machine （KELM）. By using KELM for training V-I data to filter noise, the accuracy of MhA fitness function is promoted, giving impetus to the global exploration ability of MhA, as a result, the parameter identification precision is well guaranteed. In the validation experiments, the solar cell double diode model （DDM） is used for parameter identification, to be more specific, 50 sets of mixed V-I data were subjected to both non-denoising and denoising processing, and then the parameter identification results of 6 MhA by different processing methods were put in comparison. According to the experimental results, DDMhA is effective in filtering data noise, thus improving the identification accuracy and convergence speed of the original MhA.

Key words

solar cells / parameter identification / heuristic algorithms / data denoising / kernel extreme learning machine（KELM）

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Gu Minqi, Lyu Zhilin, Lu Jianfeng, Hai Tao, Wang Jun. PARAMETER IDENTIFICATION OF SOLAR CELL BASED ON DATA DENOISING META-HEURISTIC ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 589-597 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0357

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