Application of artificial neuronal networks in extracting parameters of solar cells

IF 0.9 4区 物理与天体物理 Q4 PHYSICS, APPLIED
M. Khalis, R. Masrour
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引用次数: 4

Abstract

This paper presents a new neural network-based approach that aims to use the back propagation multilayer perceptual (MLP) propagation algorithm to improve the extraction of parameters from a solar cell based on the single-diode model from the experimentally measured characteristic I (V ). The I (V ) current function as a model function for the neural network, is used the Lambert function W and I can be expressed as an explicit function. The main five parameters of interest of the function I (V ) are the photocurrent, I ph , the saturation current in inverse diode, I 0 , the ideality factor of the diode, n , the resistance in series, RS and shunt resistance, R Sh . We have used the Matlab to find the five parameters of the cell. To verify the proposed approach, we chose two different solar cells made of mono- and polycrystalline silicon. The comparison between the measured values and the results of the proposed model shows great precision. Finally, the values found of the five parameters by our approach are compared with those found by the method of least squares (MLS).
人工神经网络在太阳能电池参数提取中的应用
本文提出了一种新的基于神经网络的方法,旨在使用反向传播多层感知(MLP)传播算法来改进基于单二极管模型的太阳能电池参数从实验测量的特性I (V)中提取的方法。I (V)电流函数作为神经网络的模型函数,使用朗伯特函数W, I可以表示为显式函数。函数I (V)主要关心的五个参数是光电流I ph、反向二极管饱和电流I 0、二极管理想因数n、串联电阻RS和并联电阻R Sh。我们使用Matlab找到了电池的五个参数。为了验证所提出的方法,我们选择了由单晶硅和多晶硅制成的两种不同的太阳能电池。实测值与模型计算结果的比较表明,该模型具有较高的精度。最后,将该方法得到的5个参数值与最小二乘法得到的值进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.90
自引率
10.00%
发文量
84
审稿时长
1.9 months
期刊介绍: EPJ AP an international journal devoted to the promotion of the recent progresses in all fields of applied physics. The articles published in EPJ AP span the whole spectrum of applied physics research.
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