Pitch Control of Wind Turbines Using IT2FL Controller Versus T1FL Controller

Abstract

Pitch control is one of the most important issues in modern wind turbines. It is vital to regulate the generator output power and reduce the fatigue load in related parts of wind turbine, also preventing from possible dangers due to an unpredictedincrease of wind speed and output power. The most recent approach in pitch control is to use of fuzzy controllers. An important feature of fuzzy controllers is the ability to solve nonlinear problems. However, the Type-1 Fuzzy Logic (T1FL) controllers cannot show uncertainly of parameters in pitch control. In this study, Interval Type-2 Fuzzy Logic (IT2FL) is applied instead of the T1FL to include and represent high levels of uncertainties in problem parameters in order to increase the accuracy of the results. The results indicate that the IT2FL controller in compare with T1FL controller has better improvement in adjustment of pitch angle, controlling of rotor speed and optimizing output power to achieve rated power in the generator.

Keywords

• Wind Turbine; Pitch Control; Interval Type-2 Fuzzy Logic; Type-1 Fuzzy Logic.

References

1. O. Suvire, Wind Farm – Technical Regulations, Potential Estimation and Siting Assessment, 1st ed. Croatia: InTech Ltd, 2011.
2. GWEC-Team, "Wind Energy Statistics 2012," Global Wind Energy Council, Belgium2013.
3. Chia Chen Ciang, Jung-Ryul Lee, and H.-J. Bang, "Structural health monitoring for a wind turbine system: a review of damage detection methods," Measurement Science and Technology, vol. 19, 2008.
4. S Soua, B Bridge, L Cebulski, and T.-H. Gan, "Statistical analysis of accelerometer data in the online monitoring of a power slip ring in a wind turbine," Measurement Science and Technology, vol. 23, 2012.
5. T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind Energy Handbook, 2nd ed. United Kingdom: A John Wiley and Sons, Ltd., Publication, 2011.
6. A. Abdelkafi and L. Krichen, "New strategy of pitch angle control for energy management of a wind farm," Energy, vol. 36, pp. 1470-1479, 2011.
7. A. Hwas and R. Katebi, "Wind Turbine Control Using PI Pitch Angle Controller. IFAC Conference on Advances in PID Control," presented at the IFAC Conference on Advances in PID Control, Brescia, Italy, 2012.
8. T. Li, A. J. Feng, and L. Zhao, "Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data- Driven Control," Journal of Control Science and Engineering, vol. 2012, pp. 1-8, 2012.

9. A. S. Yilmaz and Z. Özer, "Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks," Expert Systems with Applications, vol. 36, pp. 9767- 9775, 2009.
10. M. Fard, R. Rahmani, and M. W. Mustafa, "Fuzzy Logic Based Pitch Angle Controller for Variable Speed Wind Turbine," presented at the IEEE Student Conference on Research and Development, Malaysia, 2011.
11. M.-H. Chiang, "A novel pitch control system for a wind turbine driven by a variable-speed pump-controlled hydraulic servo system," Mechatronics, vol. 21, pp. 753-761, 2011.
12. M. B. Kadri and S. Khan, "Fuzzy Adaptive Pitch Controller of a Wind Turbine," presented at the Multitopic International, 2012. (INMIC), 2012 15th
13. O. Castillo, Type-2 Fuzzy Logic in Intelligent Control Applications. Heidelberg, 2012.
14. Springer-Verlag Berlin
15. R. Sepşlveda, O. Montiel, O. Castillo, and P. Melin, "Embedding a high speed interval type-2 fuzzy controller for a real plant into an FPGA," Applied Soft Computing, vol. 12, pp. 988-998, 2012.
16. Y. Maldonado, O. Castillo, and P. Melin, "Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications," Applied Soft Computing, vol. 13, pp. 496-508, 2013.
17. L. Shuqin, "Magnetic Suspension and Self-pitch for Vertical-axis Wind Turbines," Fundamental and Advanced Topics in Wind Power, pp. 233-248, 2011.
18. Hyung Suk Kang and C. Meneveau, "Direct mechanical torque sensor for model wind turbines," Measurement Science and Technology, vol. 21, 2010.
19. R. B. Gavino, J. Christpher Y.Ng, R. L. Ng, and B. L. Tan, "Development of an automated wind turbine using fuzzy logic," DLSU engineering e-journal, vol. 1, pp. 28-42, 2007.
20. M. Zamani, H. Nejati, A. T. Jahromi, A. R. Partovi, S. H. Nobari, and G. N. Shirazi, "Toolbox for Interval Type-2 Fuzzy Logic Systems," Atlantis Press, 2008.
21. O. Castillo and P. Melin, Type-2 Fuzzy Logic: Theory and Applications. USA: Verlag Berlin Heidelberg, 2008.
22. D. Wu, "A Brief Tutorial on Interval Type-2 Fuzzy Sets and Systems," University of Southern California, USA2012.
23. E. P. Dadios, Fuzzy Logic-Algorithms, Techniques and Implementations, 1st ed. Croatia: InTech Ltd., 2012.
24. D. Hidalgo , P. Melin , and O. Castillo "An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms," Expert Systems with Applications, vol. 39, pp. 4590–4598, 2012.
25. J. Jantzen, "Design Of Fuzzy Controllers," Technical University of Denmark, Department of Automation, Denmark1998.
26. A. M. Fanelli, W. Pedrycz, and A. Petrosino, Fuzzy Logic and Applications, 1st. ed. USA: Springer-Verlag Berlin Heidelberg, 2011.
27. O. Castillo and P. Melin, Recent Advances in Interval Type-2 Fuzzy Systems vol. 1. USA: Springer, 2012.
28. J. R. Castro, O. Castillo, and L. G. Martínez, "Interval Type-2 Fuzzy Logic Toolbox," Engineering Letters, vol. 15, 2007.
29. Vestas®Experts, "V80-2.0 MW Wind Turbine," in Vestas® Wind Systems, Vestas®, Ed., 1st ed. Denmark: Vestas® Company, 2011.
30. S. Miller, "Wind Turbine Model (R2012b)," R2012b ed. (http://www.mathworks.de/matlabcentral/fileexchange /25752-wind-turbine-model): MATLAB Central, 2009 (Updated 15 Mar 2013). File Exchange