Fuzzy Logic Control Versus Traditional PI Control Applied to a Fixed Speed Horizontal Axis Wind Turbine

  • Luis Alberto Torres SalomaoEmail author
  • Hugo Gámez Cuatzin
  • Juan Anzurez Marín
  • Isidro Ignacio Lázaro Castillo
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)


A comparison between three types of control algorithms for a 1.5 MW horizontal axis fixed speed wind turbine is presented. A fuzzy logic proportional integral control (Fuzzy PI), a fuzzy logic control (FLC) and a classical proportional integral (PI) control are tested. A robustness test by adding noise to the wind speed signal is also performed. Design of the proposed Fuzzy PI control algorithm was achieved via tuning with the Ziegler-Nichols approach, using the same methodology for the PI controller tuning with the difference of incorporating a fuzzy logic section. The fuzzy logic section selects the desired PI gains according to wind speed with a smooth control transition. Fuzzy logic control was designed to obtain maximum power extraction at low wind speeds and to limit power extraction at a 1.5 MW nominal power set point. Aerodynamic characteristics of the wind turbine were studied in order to gain a basic understanding of the system dynamics. A 1.5 MW horizontal axis wind turbine model was designed for tuning as well as simulation performance studies. Results demonstrate the effectiveness of all techniques, achieving a controlled power extraction near the nominal value for the three controllers and maximum power extraction in low wind speeds for the Fuzzy PI and FLC control algorithms.


Fuzzy logic control Non-linear control Proportional integral control Renewable energy production Wind power Wind turbine 


  1. 1.
    Barroso LA, Rudnick H, Sensfuss F, Linares P (2010) The green effect. IEEE Power Energy, IEEE PES, EUA, 8(5):22–35Google Scholar
  2. 2.
    Anzurez-Marin J, Torres-Salomao LA, Lázaro-Castillo II (2011) Fuzzy logic control for a two tanks hydraulic system model. In: Proceedings 2011 IEEE electronics, robotics and automotive mechanics conference CERMA 2011, Cuernavaca, MexicoGoogle Scholar
  3. 3.
    Torres-Salomao LA, Gámes-Cuatzin H, Anzurez-Marín J, Lázaro-Castillo II (2012) Fuzzy-PI control, PI control and fuzzy logic control comparison applied to a fixed speed horizontal axis 1.5 MW wind turbine. In: Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science, WCECS 2012, San Francisco, USA, 24–26 Oct 2012, pp 1181–1186Google Scholar
  4. 4.
    Hong SK, Nam Y (2003) An LMI-based fuzzy sate feedback control with multi-objectives. KSME Int J, Springer, Korea, pp 105–113Google Scholar
  5. 5.
    Torres-Salomao LA, Gámez-Cuatzin H (2012) Fuzzy logic control and PI control comparison for a 1.5 MW horizontal axis wind turbine. In: 16th International conference on system theory, control and computing, ICSTCC, Control Society, Sinaia, Romania, pp 1–6Google Scholar
  6. 6.
    Passino KM, Yurkovich S (1998) Fuzzy control. Addison-Wesley, USAGoogle Scholar
  7. 7.
    Burton T, Sharpe D, Jenkins N, Bossanyi E (2001) Wind energy handbook. Wiley, EnglandCrossRefGoogle Scholar
  8. 8.
    Johnson GL (2006) Wind energy systems. Electronic edition, USAGoogle Scholar
  9. 9.
    Kyoungsoo R, Choi H (2004) Application of neural network controller for maximum power extraction of a grid-connected wind turbine. SpringerGoogle Scholar
  10. 10.
    Saad-Saoud Z, Jenkins N (1995) Simple wind farm dynamic model. IEEE Proc Gener Transm Distrib 142(5):545–548CrossRefGoogle Scholar
  11. 11.
    Åström KJ, Hägglund TH (1995) New tuning methods for PID controllers. In: Proceedings of the 3rd European control conference, pp 2456–2462Google Scholar
  12. 12.
    Lázaro-Castillo II (2008) Ingeniería de Sistemas de Control Continuo. UMSNH, COECyT Michoacán, FIE, MexicoGoogle Scholar
  13. 13.
    Heske T, Neporent J (1996) Fuzzy logic for real world design. Annabooks, San DiegoGoogle Scholar
  14. 14.
    Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8:199–249MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Luis Alberto Torres Salomao
    • 1
    Email author
  • Hugo Gámez Cuatzin
    • 2
  • Juan Anzurez Marín
    • 3
  • Isidro Ignacio Lázaro Castillo
    • 3
  1. 1.ACSEUniversity of SheffieldSheffieldUK
  2. 2.CIDESI, CONACyTDesarrollo San Pablo, Santiago de QuerétaroMexico
  3. 3.FIEUniversidad Michoacana de San Nicolás de Hidalgo, Edificio Ohmega 2 Ciudad UniversitariaFelícitas del Río, MoreliaMexico

Personalised recommendations