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Neuro fuzzy control on horizontal axis wind turbine

  • Wagner Barth Lenz
  • Angelo Marcelo Tusset
  • Mauricio Ap. RibeiroEmail author
  • Jose Manoel Balthazar


To maintain the level of development renewable energy sources must step-in. One alternative is wind energy, it is a clean, stable and consolidated technology driven by the movement of air masses in the planetary boundary layer. This paper aims to optimized and select airfoil for a wind turbine of horizontal axis, using the Buhl’s methodology for the design and particle swarm for optimization. To further improve the performance a neuro fuzzy controller was implemented using the database generated with the simulations. The Neuro fuzzy controller was stable and reduce in approximately 10 m/s the wind speed required to reach 1 rad/s under 25 s, from 12 to 2.67 m/s, for low wind speeds the controller can reach 1 rad/s for 1.84 m/s at 250 s where the uncontrolled wind turbine needs 3.5 m/s. In addition,the controller yields have a better performance than 12 rad/s in conditions of low wind speeds and reduce in 8.77 during constant high wind speed.


Wind turbine Neuro fuzzy Fuzzy logic controller Wind energy Aerodynamic optimization Renewable energy Green energy 



The authors acknowledge support by CNPq, CAPES, FAPESP and FA, all Brazilian research funding agencies.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Ziegler L, Gonzalez E, Rubert T, Smolka U, Melero JJ (2018) Lifetime extension of onshore wind turbines: a review covering Germany, Spain, Denmark, and the UK. Renew Sustain Energy Rev 82:1261–1271CrossRefGoogle Scholar
  2. 2.
    de Simón-Martín M, de la Puente-Gil Á, Borge-Diez D, Ciria-Garcés T, González-Martínez A (2019) Wind energy planning for a sustainable transition to a decarbonized generation scenario based on the opportunity cost of the wind energy: Spanish Iberian Peninsula as case study. Energy Proc 157:1144–1163CrossRefGoogle Scholar
  3. 3.
    Lacal-Arántegui R (2019) Globalization in the wind energy industry: contribution and economic impact of european companies. Renew Energy 134:612–628CrossRefGoogle Scholar
  4. 4.
    Wood D (2010) Small wind turbines for remote power and distributed generation. Wind Eng 34(3):241–254CrossRefGoogle Scholar
  5. 5.
    Sørensen JN (2011) Aerodynamic aspects of wind energy conversion. Ann Rev Fluid Mech 43(1):427–448ADSCrossRefGoogle Scholar
  6. 6.
    GENERAL ELECTRIC, Haliade-x offshore wind turbine platform (2018).
  7. 7.
    Freitas S, Santos T, Brito MC (2018) Impact of large scale PV deployment in the sizing of urban distribution transformers. Renew Energy 119:767–776CrossRefGoogle Scholar
  8. 8.
    Karimi M, Mokhlis H, Naidu K, Uddin S, Bakar A (2016) Photovoltaic penetration issues and impacts in distribution network—a review. Renew Sustain Energy Rev 53:594–605CrossRefGoogle Scholar
  9. 9.
    Luo N, Vidal Y, Acho L (2014) Wind turbine control and monitoring (advances in industrial control). Springer, BerlinCrossRefGoogle Scholar
  10. 10.
    Petrović V, Jelavić M, Baotić M (2015) Advanced control algorithms for reduction of wind turbine structural loads. Renew Energy 76:418–431CrossRefGoogle Scholar
  11. 11.
    Bai C-J, Wang W-C (2016) Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (HAWTs). Renew Sustain Energy Rev 63:506–519CrossRefGoogle Scholar
  12. 12.
    Ashrafi ZN, Ghaderi M, Sedaghat A (2015) Parametric study on off-design aerodynamic performance of a horizontal axis wind turbine blade and proposed pitch control. Energy Convers Manag 93:349–356CrossRefGoogle Scholar
  13. 13.
    Nagai BM, Ameku K, Roy JN (2009) Performance of a 3 kw wind turbine generator with variable pitch control system. Appl Energy 86(9):1774–1782CrossRefGoogle Scholar
  14. 14.
    Veers PS, Ashwill TD, Sutherland HJ, Laird DL, Lobitz DW, Griffin DA, Mandell JF, Musial WD, Jackson K, Zuteck M, Miravete A, Tsai SW, Richmond JL (2003) Trends in the design, manufacture and evaluation of wind turbine blades. Wind Energy 6(3):245–259. ADSCrossRefGoogle Scholar
  15. 15.
    de Goeij W, van Tooren M, Beukers A (1999) Implementation of bending-torsion coupling in the design of a wind-turbine rotor-blade. Appl Energy 63(3):191–207. CrossRefGoogle Scholar
  16. 16.
    Boukhezzar B, Siguerdidjane H (2005) Nonlinear control of variable speed wind turbines for power regulation. In: Proceedings of 2005 IEEE conference on control applications. CCA 2005. IEEE.
  17. 17.
    Jonkman WMJ, Butterfield S, Scot G (2009) Definition of a 5-MW reference wind turbine for offshore system development, Technical reportsGoogle Scholar
  18. 18.
    Zhang Z, Chen B, Nielsen SR (2017) Coupled-mode flutter of wind turbines and its suppression using torsional viscous damper. Proc. Eng. 199:3254–3259CrossRefGoogle Scholar
  19. 19.
    Holton JR, Hakim GJ (2012) An introduction to dynamic meteorology, volume 88 (international geophysics). Academic Press, New YorkGoogle Scholar
  20. 20.
    Pratumnopharat P, Leung P (2011) Validation of various windmill brake state models used by blade element momentum calculation. Renew Energy 36(11):3222–3227CrossRefGoogle Scholar
  21. 21.
    Golnary F, Moradi H (2019) Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed. Appl Math Model 65:566–585MathSciNetCrossRefGoogle Scholar
  22. 22.
    New World Wind, The wind tree (2018).
  23. 23.
    Marten D, Peukert J, Pechlivanoglou G, Nayeri C, Paschereit C (2013) Qblade: an open source tool for design and simulation of horizontal and vertical axis wind turbines. Int J Emerg Technol Adv Eng 3:264–269Google Scholar
  24. 24.
    Mahmuddin F, Klara S, Sitepu H, Hariyanto S (2017) Airfoil lift and drag extrapolation with viterna and montgomerie methods. Energy Proc 105:811–816CrossRefGoogle Scholar
  25. 25.
    Xu J, Fu Z, Bai J, Zhang Y, Duan Z, Zhang Y (2018) Study of boundary layer transition on supercritical natural laminar flow wing at high reynolds number through wind tunnel experiment. Aerosp Sci Technol 80:221–231CrossRefGoogle Scholar
  26. 26.
    Drela M (1989) Xfoil: an analysis and design system for low reynolds number airfoils. In: Mueller TJ (ed) Low Reynolds number aerodynamics. Springer, Berlin, pp 1–12Google Scholar
  27. 27.
    Morgado J, Vizinho R, Silvestre M, Pscoa J (2016) Xfoil vs cfd performance predictions for high lift low reynolds number airfoils. Aerosp Sci Technol 52:207–214CrossRefGoogle Scholar
  28. 28.
    Kumar D, Ali SF, Arockiarajan A (2018) Structural and aerodynamics studies on various wing configurations for morphing. IFAC-PapersOnLine 51(1):498–503CrossRefGoogle Scholar
  29. 29.
    Pedersen MEH. Good parameters for particle swarm optimization. Hvass Lab., Copenhagen, Denmark, Technical reports HL1001Google Scholar
  30. 30.
    Wood D (ed) (2011) Small wind turbines. Springer, LondonGoogle Scholar
  31. 31.
    Glauert H (1983) The elements of aerofoil and airscrew theory. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  32. 32.
    Schaffarczyk AP (2014) Introduction to wind turbine aerodynamics. Springer, BerlinCrossRefGoogle Scholar
  33. 33.
    Sorensen JN (2016) General momentum theory for horizontal axis wind turbines. Springer, BerlinCrossRefGoogle Scholar
  34. 34.
    Yang Y, Li C, Zhang W, Yang J, Ye Z, Miao W, Ye K (2016) A multi-objective optimization for HAWT blades design by considering structural strength. J Mech Sci Technol 30(8):3693–3703CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2020

Authors and Affiliations

  1. 1.UTFPRPonta GrossaBrazil

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