Artificial intelligence based P&O MPPT method for photovoltaic systems

The output characteristics of photovoltare arrays are nonlinear and change with the cell's temperature and solar radiation. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (PO however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In this paper, it is shown that the negative effects associated to such a drawback can be greatly reduced if the Artificial Intelligence (Al) concepts are used to improve P&O algorithm. The perturbation step is continuously approximated by using artificial neural network (ANN). By the simulation. the validity of the proposed control algorithm is proved.

[1]  Suttichai Premrudeepreechacharn,et al.  Solar-array modelling and maximum power point tracking using neural networks , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[2]  M. Vitelli,et al.  Optimizing duty-cycle perturbation of P&O MPPT technique , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[3]  Badia Amrouche,et al.  A Multilayered Neural Network Adaptive Controller for Robot Manipulators , 2005 .

[4]  J. So,et al.  Improved perturbation and observation method (IP&O) of MPPT control for photovoltaic power systems , 2005, Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 2005..

[5]  M. Vitelli,et al.  Optimization of perturb and observe maximum power point tracking method , 2005, IEEE Transactions on Power Electronics.

[6]  M. Vitelli,et al.  Increasing the efficiency of P&O MPPT by converter dynamic matching , 2004, 2004 IEEE International Symposium on Industrial Electronics.