Development and improvement of a transient temperature model of PV modules: Concept of trailing data

The development of a transient temperature model of photovoltaic (PV) modules is presented in this paper. Currently, there are a few steady-state temperature models targeted at assessing and predicting the PV module temperature. One of the most commonly used models is the Faiman thermal model. This model is derived from the modified Hottel-Whillier-Bliss (HWB) model for flat-plate solar-thermal collector under steady-state conditions and assumes low or no thermal mass in the modules (i.e., short time constants such that transients are neglected, and steady-state conditions are assumed). The transient extension of the Faiman model we present in this paper introduces a thermal mass, which provides two advantages. First of all, it improves the temperature prediction under dynamic conditions. Second, our transient extension to the Faiman model allows the accurate parametrization of the Faiman model under dynamic conditions. We present our model and parametrization method. Furthermore, we applied the model and parametrization method to a 1-year data set with 5-min resolved outdoor module measurements. We demonstrate a significant improvement in temperature prediction for the transient model, especially under dynamic conditions. The Faiman temperature model is a steady-state model, which inevitably is inaccurate under dynamic conditions. In this work, we develop a transient extension of the model with one extra parameter representing the thermal mass of the module. We demonstrate that by taking a short time series of irradiance and wind data (of 5 to 15 min), we can greatly improve the accuracy of the predicted temperature, especially under dynamic conditions. image