MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator

  • Authors

    • A. Sriharibabu
    • G. Srinivasa Rao
  • It is very important to have maximum power point trackers for photo voltaic systems to improve their efficiency. This paper deals with the converter based maximum power point tracking by robust backstepping controller along with the neural network. The neural network provides the output reference PV voltage to the backstepping controller. Back propagation neural network is used for a standalone photovoltaic system under robust environmental conditions. Unlike Conventional   solar-array   mathematical   model, neural network does not require any physical data for modeling since it has the superior potential to derive non-linear models without requiring the physical data of the models. In  this  paper  the maximum power point of photovoltaic module is predicted with the simulation trained back-propagation  neural network using a  random  set  of  data  collected  from  a  real  photovoltaic  array. The neural network based PV system with backstepping controller is modeled in MATLAB/Simulink. At different atmospheric conditions the developed model is simulated. The simulation results of PV system depict that with the proposed converter based controller, the maximum power is tracked accurately and successfully.

  • References

    1. [1] A.M. Noman, K. E. Addoweesh, H.M. Mashaly, “DSPACE Real-Time Implementation of MPPT-Based FLC Methodâ€, International Journal of Photoenergy Volume 2013, Article ID 549273, 11 pages.

      [2] Moacyr Aureliano Gomes de Brito, L. Galotto, Jr., L. P. Sampaio, Guilherme de A. e Melo, and C.A. Canesin, “Evaluation of the Main MPPT Techniques for Photovoltaic Applicationsâ€, IEEE Trans. on Industrial Electronics, vol. 60, no. 3, March 2013,1156-1167.

      [3] Jubaer Ahmed, Z.l Salam, “An Enhanced Adaptive P&O MPPT for Fast and Efficient Tracking Under Varying Environmental Conditionsâ€, IEEE Trans. on Sustainable Energy, vol. 9, no. 3, July 2018, 1487-1496.

      [4] AI-Amoudi and Zhang, L. “Application of Radial Basis Function Networks For Solar-Array Modelling And Maximum Power-Point Predictionâ€, IEE Proceeding - Generation, Transmission and Distribution, 2000, 147,5, 310-316.

      [5] H. El Fadil, F. Giri, “Backstepping Based Control of PWM DC-DC Boost Power Convertersâ€, 2007 IEEE International Symposium on Industrial Electronics, Vigo, 2007, pp. 395-400.

      [6] A. D. Martin, J. R. Vazquez. "MPPT algorithms comparison in PV systems: P&O, PI, neuro-fuzzy and backstepping controls", 2015 IEEE International Conference on Industrial Technology (ICIT), 2015.

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  • How to Cite

    Sriharibabu, A., & Rao, G. S. (2018). MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator. International Journal of Engineering & Technology, 7(4.24), 129-132.