Nonlinear Fractional Intelligent Controller for Photovoltaic Inverters

At present, with the significant growth of energy consumption, increase of greenhouse gases and environmental pollutants, more attention is directed toward renewable energies. Renewable energies include geothermal, wind, photovoltaic energy and etc. Among the advantages of photovoltaic energy, its wide range and easy access, helping to preserve the environment, compatibility with distributed power networks, low noise, quick installation and lower cost compared to other energies can be noted. Important challenges facing photovoltaic systems are changing climatic conditions and parameters variation that affect the performance of the system. In this paper, to track the maximum power point in a photovoltaic system, a fuzzy fractional order sliding mode controller based on disturbance observer and uncertainty estimator using neural network is designed. The sliding mode control is used to reduce chattering, neural network to estimate the system uncertainties, fuzzy system to estimate the coefficient of the signum function in the control law and disturbance observer to approximate the disturbances in the system. Also, the stability of the system has been proven using the Lyapunov method. The simulation results of the photovoltaic system confirm the effectiveness of the proposed method and shows satisfactory performance.