Application of Artificial Neural Networks for Maximal Power Point Tracking
Résumé
In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.
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