H∞ Fuzzy Integral Power Control for DFIG Wind Energy System

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.

Authors:

• N. Chayaopas
• W. Assawinchaichote

Keywords:

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

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.