一种基于等效辐照度与温度的光伏组件性能估算新方法

The yield of photovoltaic (PV) modules is influenced by various environmental factors, particularly solar irradiance and temperature. However, the measured solar irradiance does not accurately represent the real light intensity absorbed by different types of solar cells, and the measured temperature does not represent the actual cell temperature in PV modules. In this article, equivalent irradiance and temperature are proposed and used to improve the accuracy of output performance estimation of PV modules. First, equivalent irradiance and temperature under different operating condition are obtained by fitting measured I–V data by using the guaranteed convergence particle swarm optimization. Second, the relationship between the equivalent irradiance and temperature and environmental factors is established by an artificial neural network (ANN) model. Two types of ANNs with different input vector are proposed to calculated equivalent irradiance and temperature. The accuracy of the proposed method was validated by experimental data for four different types of PV modules under wide operating conditions.