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The performances of a photovoltaic (PV) module connected to a load through a conversion stage (chopper, inverter) are linked to the average electricity output including the delivered power. Nevertheless, the efficiency depends on atmospheric parameters as temperature, irradiance, and wind speed. To make electrical power available, Maximum Power Point Trackers (MPPT) algorithms are developed to keep up the PV module at optimal operating point with regard to climatic variations. This paper proposes an assessment of Artificial Neural Networks model based on MultiLayer Perceptron (MLP) and Radial Basis Function (RBF). A comparative study with an Adaptive Neuro-Fuzzy Inference System and a hybrid neural network RBF/MLP is done using measured data to optimize the maximum power point of a photovoltaic generator.       Key words: Multilayer perceptron, radial basis function, maximum power point trackers, neuro-fuzzy.

A comparative study of maximum power point tracker approaches based on artificial neural network and fuzzy controllers