A Novel Actuator Fault-tolerant Control Strategy of DFIG-based Wind Turbines Using Takagi-Sugeno Multiple Models

  • Samir Abdelmalek
  • Ahmad Taher Azar
  • Djalel Dib
Regular Papers Intelligent Control and Applications


In this paper, a new combined fuzzy observer-based fault-tolerant tracking control scheme is proposed for a doubly fed induction generator (DFIG) based wind turbine (WT) subject to actuator faults. The main contribution consists of the proposal of a novel fault-tolerant fuzzy tracking controller combined with a nominal control law. The control objective is to ensure good state references tracking regardless of the actuator faults effects and simultaneous system state and faults estimation. This later requires the knowledge of the occurrence of actuator faults which are estimated from a Takagi-Sugeno Fuzzy Proportional Integral Observer (T-S FPIO). Within this control scheme, a T-S FPIO has been developed to provide stability tracking error dynamics even the system is subjected to different actuator faults. A compensation term is appended to the composite controller and to ensure robustness against actuator faults. Stability and tracking analysis properties are demonstrated through a quadratic Lyapunov function, which are formulated in terms of Linear Matrix Inequalities (LMIs). The observer gains are determined based on the proposed LMIs stability conditions. A numerical simulation is carried out on a typical 1.5 MW DFIG based WT system to access the effectiveness of the proposed control scheme in comparison to the existing results.


Actuator faults robust fault estimation robust fault-tolerant control scheme Takagi-Sugeno (T-S) fuzzy model T-S fuzzy proportional integral observer 


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  1. [1]
    GWEC, Global wind energy council, Global Wind Report, Annual Market update, in Global wind report, 2014.Google Scholar
  2. [2]
    M. S. Shaker and A. A. Kraidi, “Robust fault-tolerant control of wind turbine systems against actuator and sensor faults,” Arabian Journal for Science and Engineering, vol. 42, no. 7, pp. 3055–3063, July 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    S. Abdelmalek, L. Barazane, A. Larabi, and M. Bettayeb, “A novel scheme for current sensor faults diagnosis in the stator of a DFIG described by a TS fuzzy model,” Measurement, vol. 91, pp. 680–691, September 2016. [click]CrossRefGoogle Scholar
  4. [4]
    N. Luo, Y. Vidal, and L. Acho, Wind Turbine Control and Monitoring, Springer, Berlin, 2014.CrossRefGoogle Scholar
  5. [5]
    F. D. Bianchi, R. J. Mantz, and H. D. Battista, The Wind and Wind Turbines, Springer, London, 2007.CrossRefGoogle Scholar
  6. [6]
    A. M. Ragheb and M. Ragheb, Fundamental and Advanced Topics in Wind Power, InTech, Rijeka, Croatia, 2011.zbMATHGoogle Scholar
  7. [7]
    H. Badihi, Y. Zhang, and H. Hong, “Fuzzy gain-scheduled active fault-tolerant control of a wind turbine,” Journal of the Franklin Institute, vol. 351, no. 7, pp. 3677–3706, July 2014.CrossRefzbMATHGoogle Scholar
  8. [8]
    G. Stewart and M. Lackner, “Offshore wind turbine load reduction employing optimal passive tuned mass damping systems,” IEEE Trans on Control Systems Technology, vol. 21, no. 4, pp. 1090–1104, July 2013. [click]CrossRefGoogle Scholar
  9. [9]
    S. Simani and P. Castaldi, “Data-driven and adaptive control applications to a wind turbine benchmark model,” Control Engineering Practice, vol. 21, no. 12, pp. 1678–1693, December 2013. [click]CrossRefGoogle Scholar
  10. [10]
    P. F. Odgaard, J. Stoustrup, and M. Kinnaert, “Faulttolerant control of wind turbines: a benchmark model,” IEEE Trans on Control Systems Technology, vol. 21, no. 4, pp. 1168–1182, July 2013. [click]CrossRefGoogle Scholar
  11. [11]
    M. Sami and R. J. Patton, “Fault tolerant adaptive sliding mode controller for wind turbine power maximisation,” Proc. of IFAC, vol. 45, no. 13, pp. 499–504, 2012.CrossRefGoogle Scholar
  12. [12]
    S. Abdelmalek, L. Barazane, and A. Larabi, “An advanced robust fault-tolerant tracking control for a doubly fed induction generator with actuator faults,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 25, no. 2, pp. 1346–1357, 2017. [click]CrossRefGoogle Scholar
  13. [13]
    F. Shi and R. Patton, “An active fault tolerant control approach to an offshore wind turbine model,” Renewable Energy, vol. 75, pp. 788–798, March 2015. [click]CrossRefGoogle Scholar
  14. [14]
    S. Simani, “Overview of modelling and advanced control strategies for wind turbine systems,” Energies, vol. 8, no. 12, pp. 13395–13418, November 2015. [click]CrossRefGoogle Scholar
  15. [15]
    Y. Vidal, C. Tutivén, J. Rodellar, and L. Acho, “Fault diagnosis and fault-tolerant control of wind turbines via a discrete time controller with a disturbance compensator,” Energies, vol. 8, no. 5, pp. 4300–4316, May 2015. [click]CrossRefGoogle Scholar
  16. [16]
    M. S. Shaker and R. J. Patton, “A fault tolerant control approach to sustainable offshore wind turbines,” Wind Turbine Control and Monitoring, Springer, pp. 157–190, August 2014.Google Scholar
  17. [17]
    M. S. Shaker and R. J. Patton, “Active sensor fault tolerant output feedback tracking control for wind turbine systems via T–S model,” Engineering Applications of Artificial Intelligence, vol. 34, pp. 1–12, September 2014. [click]CrossRefGoogle Scholar
  18. [18]
    X. Wei, M. Verhaegen, and T. van den Engele, “Sensor fault diagnosis of wind turbines for fault tolerant,” Proc. of IFAC, vol. 41, no. 2, pp. 3222–3227, 2008.CrossRefGoogle Scholar
  19. [19]
    X. Wei, M. Verhaegen, and T. van Engelen, “Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques,” International Journal of Adaptive Control and Signal Processing, vol. 24, no. 8, pp. 687–707, August 2010. [click]MathSciNetzbMATHGoogle Scholar
  20. [20]
    K. Elkhatib, A. Aitouche, and R. Ghorbani, “Robust fuzzy fault-tolerant control of wind energy conversion systems subject to sensor faults,” IEEE Trans. on Sustainable Energy, vol. 3, no. 2, pp. 231–241, April 2012. [click]CrossRefGoogle Scholar
  21. [21]
    K. Elkhatib, M. Oueidat, A. Aitouche, and R. Ghorbani, “Robust scheduler fuzzy controller of DFIG wind energy systems,” IEEE Trans. on Sustainable Energy, vol. 4, no. 3, pp. 706–715, February 2013. [click]CrossRefGoogle Scholar
  22. [22]
    M. Sami and R. J. Patton, “Wind turbine power maximisation based on adaptive sensor fault tolerant sliding mode control,” Proc. of 20th Mediterranean Conference on Control & Automation (MED), August 2012.Google Scholar
  23. [23]
    H. Li, C. Yang, Y. G. Hu, B. Zhao, M. Zhao, and Z. Chenb, “Fault-tolerant control for current sensors of doubly fed induction generators based on an improved fault detection method,” Measurement, vol. 47, pp. 929–937, January 2014.CrossRefGoogle Scholar
  24. [24]
    H. Schulte and E. Gauterin, “Fault-tolerant control of wind turbines with hydrostatic transmission using Takagi–Sugeno and sliding mode techniques,” Annual Reviews in Control, vol. 40, pp. 82–92, 2015. [click]CrossRefGoogle Scholar
  25. [25]
    S. Abdelmalek, L. Barazane, and A. Larabi, “Fault diagnosis for a doubly fed induction generator,” Revue Roumaine Des Sciences Techniques-Serie Electrotechnique et Energetique, vol. 61, no. 2, pp. 159–163, May 2016.Google Scholar
  26. [26]
    S. Abdelmalek, L. Barazane, A. Larabi, and H. Belmili, “Contributions to diagnosis and fault tolerant control based on proportional integral observer: application to a doublyfed induction generator,” Proc. of the 4th IEEE International Conference on Electrical Engineering, pp. 1–5, February 2016.Google Scholar
  27. [27]
    T. Bakka and H. R. Karimi, “Wind turbine modeling using the bond graph,” Proc. of IEEE International Symposium on Computer-Aided Control System Design (CACSD), pp. 1208–1213, 28–30 October 2011.Google Scholar
  28. [28]
    M. Kamyar, “Takagi-Sugeno fuzzy modeling for process control industrial automation,” Robotics and Artificial Intelligence (EEE8005), School of Electrical, Electronic and Computer Engineering, 2008.Google Scholar
  29. [29]
    S. Aouaouda, M. Chadli, M. Boukhnifer, and H. R. Karimi, “Robust fault tolerant tracking controller design for vehicle dynamics: a descriptor approach,” Mechatronics, vol. 30, pp. 316–326, September 2015. [click]CrossRefGoogle Scholar
  30. [30]
    M. Chadli, S. Aouaouda, H. R. Karimi, and P. Shid, “Robust fault tolerant tracking controller design for a VTOL aircraft,” Journal of the Franklin Institute, vol. 350, no. 9, pp. 2627–2645, November 2013. [click]MathSciNetCrossRefzbMATHGoogle Scholar
  31. [31]
    T. Youssef, M. Chadli, H. R. Karimi, and M. Zelmat, “Design of unknown inputs proportional integral observers for TS fuzzy models,” Neurocomputing, vol. 123, pp. 156–165, January 2014. [click]CrossRefGoogle Scholar
  32. [32]
    J. Lfberg, “YALMIP: A toolbox for modeling and optimization in MATLAB,” Proc. of IEEE International Symposium on Computer Aided Control Systems Design, pp. 284–280, 2004.Google Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Samir Abdelmalek
    • 1
  • Ahmad Taher Azar
    • 2
    • 3
  • Djalel Dib
    • 4
  1. 1.Faculty of Technology, Department of ElectronicsHassiba Benbouali University of ChlefChlefAlgeria
  2. 2.Faculty of Computers and InformationBenha UniversityBenhaEgypt
  3. 3.School of Engineering and Applied SciencesNile University, Sheikh Zayed District, 6th of October CityGizaEgypt
  4. 4.Department of Electrical EngineeringUniversity Larbi TebessiTebessaAlgeria

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