基于非线性最小二乘法的光伏电池参数辨识

发布时间：2018-08-07 18:17

【摘要】：光伏阵列模型的准确性直接关系到其仿真的可靠性,由于模型的参数值随工况的变化而改变,因此根据实测数据辨识模型参数尤为重要。本文采用四参数模型对光伏组件建模,首先提出了一种基于逐步线性搜索的非线性最小二乘光伏电池参数辨识方法,利用该方法辨识得到了光伏电池的参数参考值,辨识曲线与实测数据的对比结果证明了该方法的可行性,且辨识精度较高,实现简单。然后对光伏组件四参数模型中的α、β、γ3个系数进行了优化,并将光伏电池参数辨识与模型系数优化相结合,提出了参数辨识-系数优化(PI-CO)迭代法。迭代过程中参数值和系数值快速收敛至最优点,且目标函数值随之减小,表明该辨识方法能够进一步提高仿真精度。
[Abstract]:The accuracy of photovoltaic array model is directly related to the reliability of its simulation. Because the parameters of the model vary with the operating conditions, it is particularly important to identify the model parameters based on the measured data. In this paper, a four-parameter model is used to model the photovoltaic module. Firstly, a nonlinear least square photovoltaic cell parameter identification method based on stepwise linear search is proposed, and the parameter reference value of the photovoltaic cell is obtained by using this method. The comparison between the identification curve and the measured data proves the feasibility of the method, and the accuracy of identification is high and the realization is simple. Then the 伪, 尾 and 纬 coefficients of the four-parameter model of photovoltaic module are optimized, and the parameter identification of photovoltaic cell is combined with the optimization of the model coefficient, and the parameter identity-coefficient optimization (PI-CO) iteration method is proposed. The parameter value and coefficient value converge to the best in the iterative process, and the value of the objective function decreases, which indicates that the identification method can further improve the simulation accuracy.
【作者单位】：国网青海省电力公司电力科学研究院;青海省光伏发电并网技术重点实验室;
【基金】：青海省电力公司项目(52280714001D) 青海省光伏发电并网技术重点实验室项目(2014-Z-Y34A)
【分类号】：TM914.4

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