一种用于计算光伏组件温度的多元非线性函数拟合与修正方法

摘要

In order to obtain the module temperature of PV array, by analyzing the relationship between module temperatures and influencing factors, the function law between various external environmental factors and module temperatures is determined. Combining multivariate nonlinear function fitting with BP neural network, this paper proposes a more comprehensive method to calculate the temperature of PV modules. Aiming at the partial shading of PV arrays, the method is modified by analyzing the relationship between temperature of modules and the maximum power point. The actual data from a photovoltaic power plant verify that the method has high accuracy and strong practicability. It can reflect the changes in the external environment and has a strong self-adaptivity.%为了获得光伏阵列的组件温度，通过分析组件温度与影响因素之间的关系，确定了各种外界环境因素与组件温度的函数规律。将多元非线性函数拟合与BP神经网络相结合，提出一种计算光伏组件的温度的方法。针对光伏组件局部遮阴的情况，通过研究组件温度与最大功率点之间的关系，对该方法做出了修正。经过光伏电站实际数据验证得知，方法计算精度高，能够反映外界环境变化，具有较强的自适应性和实用性。