  Introduction. This article deals with the optimization of the energy
conversion of a grid-connected photovoltaic system. The novelty is to develop
an intelligent maximum power point tracking technique using artificial neural
network algorithms. Purpose. Intelligent maximum power point tracking technique
is developed in order to improve the photovoltaic system performances under the
variations of the temperature and irradiation. Methods. This work is to
calculate and follow the maximum power point for a photovoltaic system
operating according to the artificial intelligence mechanism is and the latter
is used an adaptive modified perturbation and observation maximum power point
tracking algorithm based on function sign to generate an specify duty cycle
applied to DC-DC converter, where we use the feed forward artificial neural
network type trained by Levenberg-Marquardt backpropagation. Results. The
photovoltaic system that we chose to simulate and apply this intelligent
technique on it is a stand-alone photovoltaic system. According to the results
obtained from simulation of the photovoltaic system using adaptive modified
perturbation and observation artificial neural network the efficiency and the
quality of the production of energy from photovoltaic is increased. Practical
value. The proposed algorithm is validated by a dSPACE DS1104 for different
operating conditions. All practice results confirm the effectiveness of our
proposed algorithm.
