The optimization of the photovoltaic (PV) system performance is done through the energy management stage. The first step of the optimization consists in extracting the maximum power available from the PV generator. This is done by the Maximum Power Point Tracking (MPPT) of the PV generator which varies with the irradiance, the temperature and the load. Several techniques as the 'Perturbation and observation' (PO), the 'Incremental conductance' (INC), the 'Power feed-back method', the 'Hill climbing' are used for this tracking.
In this article, we present a new MPPT algorithm, based on neural network controller (NNC), whose structure configuration is of three layer; an input layer, an output layer and a hidden layer. In order to show the functionality of the developed neural controller algorithm, we have applied it to a standalone PV system for several conditions of irradiance and temperature. The tests conducted on this NNC show that it allows the system to reach quickly the optimal performance with a stable pattern for all the cases considered.