Neural network estimation of photovoltaic I–V curves under partially shaded conditions

In this paper, we present a neural network algorithm to estimate the I-V curve of a photovoltaic (PV) module under non-uniform temperature and shading distributions. We first present a novel photovoltaic simulation model which includes the interaction of (1) heat transfer including conduction, convection, and radiation (long and short wavelength), (2) an electro-optical two diode model including ohmic heat dissipation, and (3) environmental influences including shading, irradiance, and wind dependencies. The neural network trains on inputs which consist of shading and temperature patterns of each cell of the module, and predicts the current versus voltage and power versus voltage landscapes. This information can be used for maximum power point tracking under non-uniform conditions. The neural network was validated on the simulation model and on data collected from our rooftop PV lab.