H∞ Fuzzy Integral Power Control for DFIG Wind Energy System

Authors: N. Chayaopas, W. Assawinchaichote

Abstract:

In order to maximize energy capturing from wind energy, controlling the doubly fed induction generator to have optimal power from the wind, generator speed and output electrical power control in wind energy system have a great importance due to the nonlinear behavior of wind velocities. In this paper purposes the design of a control scheme is developed for power control of wind energy system via H∞ fuzzy integral controller. Firstly, the nonlinear system is represented in term of a TS fuzzy control design via linear matrix inequality approach to find the optimal controller to have an H∞ performance are derived. The proposed control method extract the maximum energy from the wind and overcome the nonlinearity and disturbances problems of wind energy system which give good tracking performance and high efficiency power output of the DFIG.

Keywords: H∞ fuzzy integral control, linear matrix inequality, wind energy system, doubly fed induction generator (DFIG).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130547

References:

[1] D. Cooper, “Website of the Johnson Cornell University, Johnsons Energy Club Competes in Renewable Energy, https://www.johnson.cornell.edu,” 2012.
[2] Y. Guo, S. Hosseini, C. Tang, J. Jiang, and R. Ramakumar, “An approximate wind turbine control system model for wind farm power control,” IEEE Transactions on Sustainable Energy, vol. 4, no. 1, pp. 262–274, 2013.
[3] L. Mihet-Popa, F. Blaabjerg, and I. Boldea, “Network power flux control of a wind generator,” IEEE Transactions on Industry Applications, vol. 40, no. 1, pp. 3–10, 2004.
[4] D. Aouzellag, K. Ghedamsi, and E. Berkouk, “Network power flux control of a wind generator,” Journal of Renewable Energy, vol. 34, no. 3, pp. 615–622, 2009.
[5] J. Ben Alaya, A. Khedher, and M. Mimouni, “DTC, DPC and nonlinear vector control strategies applied to DFIG operated at variable speed,” WSEAS transaction on environment and development, vol. 6, no. 11, pp. 744–753, 2010.
[6] M. Ma, H. Chen, X. Liu, and F. Allg¨ower, “Moving horizon h∞ control of variable speed wind turbines with actuator saturation,” Journal of IET Renewable Power Generation, vol. 8, no. 5, pp. 498–508, 2014.
[7] S. Muller, M. Deicke, and R. D. Doncker, “Doubly fed induction generator systems for wind turbines,” IEEE Industry Applications Magazine, vol. 8, no. 3, pp. 26–33, 2002.
[8] A. Peterson, “Analysis, modeling and control of doubly-fed induction generators for wind turbines,” Ph.D. dissertation, Chalmers University of Technology, Goteborg, Sweden, 2005.
[9] A. Junyent-Ferr´e, O. Gomis-Bellmunt, A. Sumper, M. Sala, and M. Mata, “Modeling and control of the doubly fed induction generator wind turbine,” International Journal of Simulation Modelling Practice and Theory, vol. 18, no. 9, pp. 1365–1381, 2010, dOI: 10.1016/j.simpat.2010.05.018.
[10] S. Kovendan and P. Sebastian, “High efficiency control for a wind energy conversion system with induction generator,” International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 12, pp. 262–265, 2014.
[11] W. Dinghui, L. Yiyang, J. Zhicheng, and S. Yanxia, “Self-adaptive PID optimal control of wind energy conversion system,” in Proceedings of Control and Decision Conference, CCDC 2014, Changsha, China, 2014, pp. 13–17.
[12] H. Nguyen and S. Naidu, “Advanced control strategies for wind energy systems,” in Power Systems Conference and Exposition, PSCE 2011, Phoenix, Arizona, 2011, pp. 1–8.
[13] T. Blanchett, G. Kember, and R. Dubay, “PID gain scheduling using fuzzy logic,” ISA Transaction, vol. 39, no. 3, pp. 317–325, 2000.
[14] V. Galdi, A. Piccolo, and P. Siano, “Designing an adaptive fuzzy controller for maximum wind energy extraction,” IEEE Transactions on Energy Conversion, vol. 23, no. 2, pp. 559–569, 2008.
[15] W. Assawinchaichote, “Further results on robust fuzzy dynamic systems with LMI D-stability constraints,” International Journal of Applied Mathematics and Computer Science, vol. 24, no. 4, pp. 785–794, 2014.
[16] P. Vadive, R. Sakthivel, K. Mathiyalagan, and P. Thangaraj, “Robust stabilisation of non-linear uncertain Takagi-Sugeno fuzzy system by H∞ control,” International Journal of IET Control Theory & Applications, vol. 6, no. 16, pp. 2556–2566, 2012.
[17] M. Aliyu, Nonlinear H∞-Control, Hamiltonian Systems, and Hamilton- Jacobi Equations. Boca Raton, FL: CRC Press, 2011.
[18] W. Assawinchaichote, “A non-fragile H∞ output feedback controller for uncertain fuzzy dynamical systems with multiple time-scales,” International Journal of Computers, Communications & Control, vol. 78, no. 1, pp. 514–531, 2012.
[19] S. Bououden, M. Chadli, S. Filali, and A. Hajjaji, “Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach,” International Journal of Renewable Energy Research, vol. 37, no. 1, pp. 434–439, 2012.
[20] M. Ragheb and A. Ragheb, “Wind turbines theory-The betz equation and optimal rotor tip speed ratio,” in Fundamental and Advanced Topics in Wind Power, C. R., Ed. Intech, 2011.
[21] M. Cheng and Y. Zhu, “The state of the art of wind energy conversion systems and technologies: A review,” Journal of Energy Conversion and Management, vol. 88, no. 2014, pp. 332–347, 2014.
[22] M. Hand and M. Balas, “Systematic controller design methodology for variable-speed wind turbines,” National Renewable Energy Laboratory (NREL), Golden, Colorado, USA, Tech. Rep. NREL/TP-500-29415, 2002.