Optimum Fuzzy Logic Control System Design using Cuckoo Search Algorithm for Pitch Control of a Wind Turbine

Optimum Fuzzy Logic Control System Design using Cuckoo Search Algorithm for Pitch Control of a Wind Turbine

Piyali Ganguly Akhtar Kalam Aladin Zayegh

Department of Engineering and Science, Victoria University, Ballarat Road, Footscray 3011, Melbourne, Australia

Corresponding Author Email: 
Piyali.ganguly@live.vu.edu.au, Akhtar.Kalam@vu.edu.au, aladin.zayegh@vu.edu.au
10 October 2017
15 November 2017
31 December 2017
| Citation



Renewable Energy Systems (RES) are being widely accepted as an alternative to standard conventional energy sources due to depletion of natural resources and their consequential environmental impact. With improving techniques, reducing costs and low environmental impact, wind energy has the potential to become the major part in the world’s energy future. The efficiency and control of wind generator is of outmost importance as wind is an intermittent resource. Pitch angle control is the most common means for adjusting the aerodynamic torque of the wind turbine when the wind speed is above rated speed.

This paper presents a methodology for designing an optimised Fuzzy Logic Controller (FLC) system using Cuckoo Search Algorithm (CSA) for enhancing the performance of wind turbine by maximizing the captured energy. The simulation results clearly show that the controller demonstrated high performance than conventional PID controller.


Wind turbine, pitch control, fuzzy logic, optimization, cuckoo search algorithm, renewable energy.

1. Introduction
2. Wind Turbine Model
3. Fuzzy Logic Controller (FLC)
4. Cuckoo Search Algorithm (CSA)
5. Fuzzy Logic Controller Design
6. Simulation results and Discussions
7. Conclusion

1. Adzic, Evgenije, et al. Maximum power search in wind turbine based on fuzzy logic control. Acta Polytechnica Hungarica, vol. 6, no. 1, 2009, pp. 131-149.

2. Salomao, Luis Alberto Torres, et al. Fuzzy PI control, PI control and fuzzy logic control comparison applied to a fixed speed horizontal axis 1.5 MW wind turbine. Proceedings of the World Congress on Engineering and Computer Science 2012 Vol II, October 24-26, 2012, San Francisco, USA. 2012, pp. 978-988.

3. Su, Dan, et al. Programme and simulation of the fuzzy control list in fuzzy control. Intelligent Control and Automation (WCICA), 2010 8th World Congress on. IEEE, 2010, pp. 1935-1940.

4. Su, Dan, et al. Programme and simulation of the fuzzy control list in fuzzy control. Intelligent Control and Automation (WCICA), 2010 8th World Congress on. IEEE, 2010, pp. 1935-1940

5. Ahmad, Aziz, et al. Liquid level control by using fuzzy logic controller, 2012.

6. Männle, Manfred. FTSM—Fast Takagi-Sugeno Fuzzy Modeling. IFAC Proceedings Volumes, vol. 33, no. 11, 2000, pp. 651-656. 

7. Sala, Antonio, Thierry Marie Guerra, and Robert Babuška. Perspectives of fuzzy systems and control. Fuzzy sets and systems, vol. 156, no. 3, 2005, pp. 432-444.

8. Chadli, Mohammed, and Hamid Reza Karimi. Robust observer design for unknown inputs Takagi–Sugeno models. IEEE Transactions on Fuzzy Systems, vol. 21, no. 1, 2013, pp. 158-164. 

9. Chadli, Mohammed, and Thierry-Marie Guerra. LMI solution for robust static output feedback control of discrete Takagi–Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, 2012, pp. 1160-1165. 

10. S. Aouaouda, M. Chadli, M.T. Khadir, and T. Bouarar, Robust fault tolerant tracking controller design for unknown inputs T-Smodels with unmeasurable premise variables, Journal of Process Control, vol. 22, no. 5, pp. 861–872, 2012.

11. Chadli, M., et al. Robust fault tolerant tracking controller design for a VTOL aircraft. Journal of the Franklin Institute, vol. 350, no. 9, 2013, pp. 2627-2645.

12. Rajabioun, Ramin. Cuckoo optimization algorithm. Applied soft computing, vol. 11, no. 8, 2011, pp. 5508-5518. 

13. Khan, Sheroz, et al. Design and implementation of an optimal fuzzy logic controller using genetic algorithm, Journal of Computer Science, vol. 4, no. 10, 2008, pp. 799.

14. Hwang, Wen-Ruey, Wiley E. Thompson. Design of intelligent fuzzy logic controllers using genetic algorithms. Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on. IEEE, 1994, pp. 1383-1388.

15. Sivanandam, S. N., and S. N. Deepa. Introduction to genetic algorithms. Springer Science & Business Media, 2007. 

16. Eberhart, Russell, and James Kennedy. A new optimizer using particle swarm theory. Micro Machine and Human Science, 1995. MHS'95. Proceedings of the Sixth International Symposium on. IEEE, 1995, pp. 39-43

17. Engelbrecht, Andries P. Fundamentals of computational swarm intelligence. John Wiley & Sons, 2006. 

18. Jr, Iztok Fister, et al. Analysis of randomisation methods in swarm intelligence. International journal of bio-inspired computation, vol. 7, no. 1, 2015, pp. 36-49. 

19. Yang, Xin-She, and Suash Deb. Cuckoo search via Lévy flights. Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, 2009. 

20. Herrera, Francisco, Manuel Lozano, and Jose L. Verdegay. Tuning fuzzy logic controllers by genetic algorithms. International Journal of Approximate Reasoning, vol. 12, no. 3-4, 1995, pp. 299-315. 

21. Zadeh, Lotfi A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy sets and systems, vol. 90, no. 2, 1997, pp. 111-127.

22. Du, Haiping, and N. Zhang. Takagi-Sugeno fuzzy modelling of multivariable nonlinear system via genetic algorithms. International Conference on Intelligent Technologies. University of Technology, Sydney, 2007, pp. 176-181.

23. Roubos, Hans, and Magne Setnes. Compact fuzzy models and classifiers through model reduction and evolutionary optimization. The practical handbook of genetic algorithms: Applications, Chapman&Hall/CRC, 2001. 

24. Zhang, Jianzhong, et al. Pitch angle control for variable speed wind turbines. Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on. IEEE, 2008, pp. 2691-2696.

25. Ro, Kyoungsoo, and Han-ho Choi. Application of neural network controller for maximum power extraction of a grid-connected wind turbine system. Electrical Engineering, vol. 88, no. 1, 2005, pp. 45-53.

26. Buaklee, Wirote, and Komsan Hongesombut. Optimal DG allocation in a smart distribution grid using Cuckoo Search algorithm. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2013 10th International Conference on. IEEE, 2013, pp. 1-6.

27. Koochaki, Amangaldi, et al. Multi-Machine Power System Fuzzy Stabilizer Design using Cuckoo Search Algorithm. Organ 3, 2016, pp. 16.

28. W. Li and B. Zhang, Fuzzy control of robotic manipulators in the presence of joint friction and loads changes, in Proc. ASME Int. Comput. Eng. Conf., San Diego, CA, Aug. 1993.

29. Gandomi, Amir Hossein, Xin-She Yang, and Amir Hossein Alavi. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, vol. 29, no. 1, 2013, pp. 17-35.

30. Ma, Jieming, et al. Parameter estimation of photovoltaic models via cuckoo search. Journal of Applied Mathematics 2013, 2013, pp. 1-8.

31. Sanajaoba, Sarangthem, and Eugene Fernandez. Maiden application of cuckoo search algorithm for optimal sizing of a remote hybrid renewable energy system. Renewable Energy, vol. 96, 2016, pp. 1-10.

32. S. Belhamdi, A.Goléa. Direct Torque Control for Induction Motor with broken bars using Fuzzy Logic Type-2 AMSE Journals-2015 -Series, Modelling C, vol. 70, no. 1, 2015, pp. 15-28.