Analytical versus neural real-time simulation of a photovoltaic generator based on a DC-DC converter

This paper presents a simulator of a PV (photovoltaic) field where the current-voltage characteristic is obtained either with a fully analytical model or with a numerical model based on a growing neural gas (GNG) network. The power stage is obtained with a DC-DC buck converter driven by the current-voltage-irradiance-temperature relation of the PV array. The improvements introduced here, respect to previous works, are the following: 1) the mathematical model is given as a continuous surface in the irradiance domain, 2) a relation between temperature and irradiance is obtained by a LSR (lease square regression) method, 3) the thermal constant of the PV field is introduced, 4) a lower number of neurons is used, 5) a better learning of the data is achieved, 6) an experimental prototype of higher rating has been devised and constructed. For both the approaches a more performing control technique of the converter has been used. Finally a PV simulator prototype is experimentally tested.