Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity

International audience ; In the field of power optimization of photovoltaic panels (PV), there exist many maximum power pointtracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based onfuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms,P&O is one of the most widely used due to its simplicity of implementation. However, the majordrawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM.Conversely, MPPT control using neural networks have shown to be a very efficient solution in term ofaccuracy. However, this approach remains complex.In this paper we propose an original optimization of the P&O MPPT control with a neural networkalgorithm leading to a significant reduction of the computational cost required to train it, ensuring a goodcompromise between accuracy and complexity. The algorithm has been applied to the models of twodifferent types of solar panels, which have been experimentally validated.