Advertisement

Maximum Power Point Tracking Control of Wind Energy Conversion Systems

  • Yong FengEmail author
  • Xinghuo Yu
Chapter
Part of the Advances in Industrial Control book series (AIC)

Abstract

This chapter studies the control problems in grid integration of wind energy conversion systems. Sliding-mode control technique will be used to optimize the control of wind energy conversion systems. The maximum power point tracking control algorithms for variable-speed wind energy conversion systems are presented. The grid integration of wind energy conversion systems can be optimized in terms of power delivered to the grid and providing the voltage support ancillary service at the point of common coupling. The control objective for the grid integration of wind energy conversion systems is to keep the DC-link voltage in a desirable value and the input or output power factors staying unitary. The high-order terminal sliding-mode voltage and current regulators are designed, respectively, to control the DC-link voltage and the current rapidly and exactly. The numerical simulations will be carried out to evaluate the control schemes.

Keywords

DFIG-based wind power system Voltage-oriented control (VOC) Grid-side PWM converter Sliding-mode control Terminal sliding mode 

Nomenclature

\(P_w\)

Input power to the wind turbine

\(r\)

Wind turbine radius

\(v_w\)

Wind speed

\(\rho\)

Air density

\(P_m \)

Mechanical power

\(C_p\)

Power coefficient

\(\beta\)

Pitch angle

\(\lambda\)

Tip speed ratio

\(\omega_w\)

Turbine angular speed

P, Q

Active and reactive power for the induction generator

\(i_{ds}, i_{qs}\)

Stator currents in d-q axes

\(u_{ds},u_{qs}\)

Stator voltages in d-q axes

L

Inductor of the grid side filter

R

Resistance of the grid side filter

C

DC-link capacitor

\(i_d, i_q\)

d- and q-axis current components of the converter

\(s_d, s_q\)

d- and q-axis switching control signals

\(e_d, e_q\)

d- and q-axis voltage component of the three-phase supply

\(\omega\)

Angular frequency of the power source

\(P_{ac}, P_{dc}\)

Active power of AC and DC sides

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61074015), and also in part by ARC Linkage Project (LP100200538) of the Australian Research Council.

References

  1. 1.
    REN21 (2011) Renewables 2011: Global Status Report. http://www.ren21.net/Portals/97/documents/GSR/GSR2011_Master18.pdf
  2. 2.
    The Encyclopedia (2011) wind energy conversion system. http://www.daviddarling.info/encyclopedia/W/AE_wind_energy_conversion_system.html
  3. 3.
    Thongam JS, Bouchard P, Beguenane R, Fofana I (2010) Neural network based wind speed sensorless MPPT controller for variable-speed wind energy conversion systems. In: Proceedings of IEEE electric power energy conference: “sustainable energy intelligent grid”, Halifax, NS, USA, 25–27 Aug 2010Google Scholar
  4. 4.
    De Battista H, Mantz RJ, Christiansen CF (2000) Dynamical sliding mode power control of wind driven induction generators. IEEE Trans Energy Convers 15(4):451–457CrossRefGoogle Scholar
  5. 5.
    Susperregui A, Tapia G, Martinez MI, Blanco A (2011) Second-order sliding-mode controller design and tuning for grid synchronization and power control of a wind turbine-driven DFIG. In: Proceedings of the IET conference on renewable power generation (RPG 2011), Edinburgh, UK, 6–8 Sept 2011Google Scholar
  6. 6.
    Hasanien HM, Muyeen SM (2012) Speed control of grid-connected switched reluctance generator driven by variable speed wind turbine using adaptive neural network controller. Electr Power Syst Res 84(1):206–213CrossRefGoogle Scholar
  7. 7.
    Yu X, Man Z (1996) Model reference adaptive control systems with terminal sliding modes. Int J Control 64(6):1165–1176CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Feng Y, Zheng J, Yu X, Truong NV (2009) Hybrid terminal sliding-mode observer design method for a permanent-magnet synchronous motor control system. IEEE Trans Ind Electron 56(9):3424–3431Google Scholar
  9. 9.
    Feng Y, Yu X, Han F (2013) On nonsingular terminal sliding-mode control of nonlinear systems. Automatica 49(6):1715–1722CrossRefMathSciNetGoogle Scholar
  10. 10.
    Feng Y, Han X, Wang Y, Yu X (2007) Second-order terminal sliding mode control of uncertain multivariable systems. Int J Control 80(6):856–862CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Feng Y, Bao S, Yu X (2004) Inverse dynamics nonsingular terminal sliding mode control of two-link flexible manipulators. Int J Robot Autom 19(2):91–102Google Scholar
  12. 12.
    Feng Y, Yu XH, Man ZH (2002) Nonsingular adaptive terminal sliding mode control of rigid manipulators. Automatica 38(12):2167–2179CrossRefMathSciNetGoogle Scholar
  13. 13.
    Feng Y, Yu X, Han F (2013) High-order terminal sliding-mode observer for parameter estimation of a permanent magnet synchronous motor. IEEE Trans Ind Electron 60(10):4272–4280CrossRefGoogle Scholar
  14. 14.
    Anderson PM, Bose A (1983) Stability simulation of wind turbine systems. IEEE Power Appar Syst PAS-102(12):3791–3795Google Scholar
  15. 15.
    Ghanes M, Zheng G (2009) On sensorless induction motor drives: sliding-mode observer and output feedback controller. IEEE Trans Ind Electron 56(9): 3404–3413Google Scholar
  16. 16.
    Feng Y, Zhou M, Wang Y, Yang Y (2013) High-order terminal sliding-mode control strategy for wind energy Integration into power network. In: Proceedings of the 32nd chinese control conference (CCC), Xi’an, China, 26–28 Jul 2013, pp 3186–3189Google Scholar
  17. 17.
    Malesani L, Rossetto L, Tomasin P (1993) AC/DC/AC PWM Converter with Minimum Energy Storage in the DC Link. In: Proceedings of IEEE applied power electronics conference APEC’93, San Diego, CA, USA, 7–11 Mar 1993, pp 306–311Google Scholar
  18. 18.
    Blasko V, Kaura V (1997) A new mathematical model and control of three-phase AC-DC voltage source converter. IEEE Trans Power Electron 12(1):116–123CrossRefGoogle Scholar
  19. 19.
    Green AW, Boys JT (1989) Hysteresis current-forced three-phase voltage-sourced reversible rectifier. IEEE Trans Power Electron 136(3):113–120Google Scholar
  20. 20.
    Kazmierkowski MP, Cichowlas M, Jasinski M (2003) Artificial intelligence based controllers for industrial PWM power converters. In: Proceedings of the IEEE international conference industrial informatics, Banff, Alta., Canada, 21–24 Aug 2003, pp 187–191Google Scholar
  21. 21.
    Konstantopoulos GC (2012) “Novel dynamic nonlinear control scheme for three-phase AC/DC voltage source converters. In: Proceedings of 2012 IEEE international conference industrial technology, ICIT 2012, Athens, Greece, 19–21 Mar 2012, pp 638–643Google Scholar
  22. 22.
    Lee TS (2003) Input-output linearization and zero-dynamics control of three-phase AC/DC voltage-source converters. IEEE Trans Power Electron 18:11–22CrossRefGoogle Scholar
  23. 23.
    Deng WH, Hu ZB (2005) “The research of decoupled state variable feedback linearization control method of three-phase voltage source PWM rectifier. Proc CSEE (China) 25(7):97–103Google Scholar
  24. 24.
    Komurcugil H, Kukrer O (2005) A novel current-control method for three-phase PWM AC/DC voltage-source converters. IEEE Trans Ind Electron 46(3):544–553CrossRefGoogle Scholar
  25. 25.
    Chen B, Feng Y, Zhou M (2013) Terminal sliding-mode control scheme for grid-side PWM converter of DFIG-based wind power system. In: Proceedings of 39th annual conference of the IEEE industrial electronics society (IECON 2013), Vienna, Austria, 10-13 Nov 2013, pp 8014–8018Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.School of Electrical and Computer EngineeringRMIT UniversityMelbourneAustralia

Personalised recommendations