Parametric identification by minimizing the squared residuals (Application to a photovoltaic cell)

B. Oukarfi, F. Dkhichi, A. Fakkar
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引用次数: 1

Abstract

In this study we develop an algorithm of nonlinear programming to identify the structural parameters of a photovoltaic cell. This algorithm adjusts at best the parameters of the cell's electrical model to the experimental measurements. Thus, to achieve this optimization, we minimize a sum of squared residuals by Gauss Newton's Method which presents an interesting rate of convergence but with sensitivity to the initial conditions. To overcome this issue, we apply, beforehand, the Least Squares Method to the two distinct parts (linear and not linear) of the IPV=f(VPV) characteristic. This first phase allows us to improve the convergence of the algorithm but not its rate. Regarding the last issue we suggest a modified version of Gauss Newton's algorithm.
残差平方最小化的参数辨识方法(在光伏电池中的应用)
在这项研究中,我们开发了一种非线性规划算法来识别光伏电池的结构参数。该算法最多调整电池电模型的参数以适应实验测量。因此,为了实现这种优化,我们通过高斯牛顿方法最小化残差平方和,该方法呈现出有趣的收敛速度,但对初始条件敏感。为了克服这个问题,我们事先将最小二乘法应用于IPV=f(VPV)特征的两个不同部分(线性和非线性)。第一阶段允许我们改进算法的收敛性,但不能提高其速度。关于最后一个问题,我们提出了高斯牛顿算法的修改版本。
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