Identification of PV model parameters using experimental and nameplate data

Abstract

In the last few decades, the use of photovoltaic systems has enormously increased. Hence tools to predict energy production are highly needed. This work presents two reliable methods for identifying the optimal parameters of a PV generating unit. In the first method, the PV system is simulated using single and double diode models. It is based on an opposition-based differential evolution algorithm where the objective function is derived from the experimental current-voltage data. In the second method, the parameters of the single diode model are identified using only datasheets provided by manufacturers. It is based on a new meta- heuristics method called Black Widow. These methods are found to be useful for designers since they are simple, fast, and accurate. The analysis is performed on different PV cells/modules and under different temperatures and irradiances. The final results are compared with different existing methods.