Frontiers in Energy

, Volume 13, Issue 1, pp 131–148 | Cite as

Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode

  • Alireza RezvaniEmail author
  • Ali Esmaeily
  • Hasan Etaati
  • Mohammad Mohammadinodoushan
Research Article


Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.


photovoltaic wind turbine hybrid system fuzzy logic controller genetic algorithm RBFNSM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rezvani A, Gandomkar M, Izadbakhsh M, Ahmadi A. Environmental/economic scheduling of a micro-grid with renewable energy resources. Journal of Cleaner Production, 2015, 87: 216–226Google Scholar
  2. 2.
    Izadbakhsh M, Gandomkar M, Rezvani A, Ahmadi A. Short-term resource scheduling of a renewable energy based micro grid. Renewable Energy, 2015, 75: 598–606Google Scholar
  3. 3.
    Rezvani A, Izadbakhsh M, Gandomkar M. Microgrid dynamic responses enhancement using artificial neural network-genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds. International Journal of Numerical Modeling, Electronic Networks, Devices and Fields, 2015, 40(40): C7-415–C7-416Google Scholar
  4. 4.
    Rezvani A, Izadbakhsh M, Gandomkar M. Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine. Energy Systems, 2015, 6(4): 551–584Google Scholar
  5. 5.
    Vafaei S, Gandomkar M, Rezvani A, Izadbakhsh M. Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances. Frontiers in Energy, 2015, 9(3): 322–334Google Scholar
  6. 6.
    Morimoto S, Nakayama H, Sanada M, Takeda Y. Sensorless output maximization control for variable-speed wind generation system using IPMSG. IEEE Transactions on Industry Applications, 2005, 41(1): 60–67Google Scholar
  7. 7.
    Ming C M, Chen C H. Intelligent control of a grid-connected windphotovoltaic hybrid power systems. Electrical Power and Energy Systems, 2014, 55(2): 554–561Google Scholar
  8. 8.
    Algazar M M, Al-Monier H, EL-halim H A, Salem M E E K. Maximum power point tracking using fuzzy logic control. International Journal of Electrical Power & Energy Systems, 2012, 39(1): 21–28Google Scholar
  9. 9.
    Liu C, Wu B, Cheung R. Advanced algorithm for MPPT control of photovoltaic systems. In: Proceeding of the Canadian Solar Buildings Conference. Montreal, Canada, 2004Google Scholar
  10. 10.
    Rai A K, Kaushika N D, Singh B, Agarwal N. Simulation model of ANN based maximum power point tracking controller for solar PV system. Solar Energy Materials and Solar Cells, 2011, 95(2): 773–778Google Scholar
  11. 11.
    Chaouachi A, Kamel R M, Nagasaka K. A novel multi-model neurofuzzy-based MPPT for three-phase grid-connected photovoltaic system. Solar Energy, 2010, 84(12): 2219–2229Google Scholar
  12. 12.
    Kharb R K, Shimi S L, Chatterji S, Ansari M F. Modeling of solar PV module and maximum power point tracking using ANFIS. Renewable and Sustainable Energy, 2014, 33(5): 602–612Google Scholar
  13. 13.
    Afsin A, Kulaksiz A. Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system. Turkish Journal of Electrical Engineering and Computer Sciences, 2012, 20(2): 241–254Google Scholar
  14. 14.
    Ben Salah C, Ouali M. Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electric Power Systems Research, 2011, 81(1): 43–50Google Scholar
  15. 15.
    Rezvani A, Izadbakhsh M, Gandomkar M. Enhancement of hybrid dynamic performance using ANFIS for fast varying solar radiation and fuzzy logic controller in high speeds wind. Journal of Electrical Systems, 2015, 11(1): 11–26Google Scholar
  16. 16.
    Vincheh R M, Kargar A, Markadeh G A. A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems. Arabian Journal for Science and Engineering, 2014, 39(6): 4715–4725Google Scholar
  17. 17.
    Izadbakhsh M, Rezvani A, Gandomkar M. Improvement of microgrid dynamic performance under fault circumstances using ANFIS for fast varying solar radiation and fuzzy logic controller for wind system. Archives of Electrical Engineering, 2014, 63(4): 551–578Google Scholar
  18. 18.
    Hadji S, Krim F, Gaubert J P. Development of an algorithm of maximum power point tracking for photovoltaic systems using genetic algorithms. In: 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). 2011, 43–46Google Scholar
  19. 19.
    Bakić V, Pezo M, Stevanović Žana, Živković M, Grubor B. Dynamical simulation of PV/Wind hybrid energy conversion system. Energy, 2012, 45(1): 324–328Google Scholar
  20. 20.
    Samia S, Ahmed G. Modeling and simulation of hybrid systems PV/Wind/Battery connected to the grid. International Conference of Automatic Control, Nantou, Taiwan, China, 2013Google Scholar
  21. 21.
    Samrat N H, Ahmad N B, Choudhury I A, Taha Z B. Modeling, control, and simulation of battery storage photovoltaic-wave energy hybrid renewable power generation systems for island electrification in Malaysia. Scientific World Journal, 2014, 2014(38): 278–279Google Scholar
  22. 22.
    Bhandari B, Poudel S R, Lee K T, Ahn S H. Mathematical modeling of hybrid renewable energy system: a review on small hydro-solarwind power generation. International Journal of Precision Engineering and Manufacturing-green Technology, 2014, 1(2): 157–173Google Scholar
  23. 23.
    Ahmed J, Salam Z. An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy, 2015, 150: 97–108Google Scholar
  24. 24.
    Chen P C, Chen P Y, Liu Y, Chen J H, Luo Y F. A comparative study on maximum power point tracking techniques for photovoltaic generation systems operating under fast changing environments. Solar Energy, 2015, 119: 261–276Google Scholar
  25. 25.
    Lin W M, Hong C M. A new Elman neural network-based control algorithm for adjustable-pitch variable speed wind energy conversion systems. IEEE Transactions on Power Electronics, 2011, 26(2): 473–481Google Scholar
  26. 26.
    Lin S C, Chen Y Y. RBF network based sliding mode control. IEEE International Conference on Systems, 1994, 2(2): 1957–1961Google Scholar
  27. 27.
    Blaabjerg F, Teodorescu R, Liserre M, Timbus A V. Overview of control and grid synchronization for distributed power generation systems. IEEE Transactions on Industrial Electronics, 2006, 53(5): 1398–1409Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Alireza Rezvani
    • 1
    • 3
    Email author
  • Ali Esmaeily
    • 2
  • Hasan Etaati
    • 3
  • Mohammad Mohammadinodoushan
    • 4
  1. 1.Department of Electrical Engineering, Saveh BranchIslamic Azad UniversitySavehIran
  2. 2.Department of Electrical Engineering, Karaj BranchIslamic Azad UniversityKarajIran
  3. 3.Iran Water and Power Resources Development Company (IWPCO)TehranIran
  4. 4.Department of Electrical Engineering, Science and Research BranchIslamic Azad UniversityTehranIran

Personalised recommendations