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. This article deals with the optimization of the energyconversion of a grid-connected photovoltaic system. The novelty is to developan intelligent maximum power point tracking technique using artificial neuralnetwork algorithms. Purpose. Intelligent maximum power point tracking techniqueis developed in order to improve the photovoltaic system performances under thevariations of the temperature and irradiation. Methods. This work is tocalculate and follow the maximum power point for a photovoltaic systemoperating according to the artificial intelligence mechanism is and the latteris used an adaptive modified perturbation and observation maximum power pointtracking algorithm based on function sign to generate an specify duty cycleapplied to DC-DC converter, where we use the feed forward artificial neuralnetwork type trained by Levenberg-Marquardt backpropagation. Results. Thephotovoltaic system that we chose to simulate and apply this intelligenttechnique on it is a stand-alone photovoltaic system. According to the resultsobtained from simulation of the photovoltaic system using adaptive modifiedperturbation and observation artificial neural network the efficiency and thequality of the production of energy from photovoltaic is increased. Practicalvalue. The proposed algorithm is validated by a dSPACE DS1104 for differentoperating conditions. All practice results confirm the effectiveness of ourproposed algorithm.

Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid