基于数据降噪启发式算法的太阳电池参数辨识

古旻琦; 吕智林; 陆剑锋; 海涛; 王钧

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

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (7) : 589-597. DOI: 10.19912/j.0254-0096.tynxb.2024-0357

第二十七届中国科协年会学术论文

基于数据降噪启发式算法的太阳电池参数辨识

古旻琦¹, 吕智林¹, 陆剑锋², 海涛¹, 王钧³

作者信息 +

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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文章历史 +

摘要

实测电压-电流（V-I）数据中的噪声会降低启发式算法（MhA）的太阳电池参数辨识精度。为解决该问题,基于核极限学习机（KELM）提出一种数据降噪启发式算法（DDMhA）以实现对太阳电池参数的精确辨识。利用KELM对V-I数据进行训练以滤除噪声,提升MhA适应度函数的准确度,增强其全局搜索能力以保障参数辨识精度。在验证实验中,采用双二极管太阳电池模型（DDM）进行参数辨识,对50 组混淆V-I数据分别进行不降噪以及降噪处理,随后对比不同处理方式下6 种MhA的参数辨识结果。根据实验结果分析,DDMhA能够滤除数据噪声,有效提升原MhA的辨识精度和收敛速度。

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.

导出引用

古旻琦, 吕智林, 陆剑锋, 海涛, 王钧. 基于数据降噪启发式算法的太阳电池参数辨识[J]. 太阳能学报. 2025, 46(7): 589-597 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0357

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

中图分类号： TM615

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