MPPT Based On Artificial Neural Networks (ANN) For a Photovoltaic System Under Unstable Environmental Conditions

In this article, an artificial neural networks (ANN) based maximum power point tracking controller (MPPT) was developed to improve the performance of the FL-M-160W solar panel under unstable environmental conditions. To develop and configure the neural controller, a database resulting from experimental tests was built for the training of the proposed model. Then the model was tested and validated under the Matlab / Simulink environment. The optimum voltage obtained at the output of the neural controller is compared to the voltage of the photovoltaic generator and the error is used to modify the duty cycle of the DC-DC boost converter. It is shown after simulations that unlike conventional controllers which are very slow, the neural MPPT controller offers more stable, more accurate output characteristics with very low response time and very low oscillations around the operating point both in transient and steady state, even under varying environmental conditions.