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Gain scheduling control of variable speed WTG under widely varying turbulence loading

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  • Billy Muhando, Endusa
  • Senjyu, Tomonobu
  • Urasaki, Naomitsu
  • Yona, Atsushi
  • Kinjo, Hiroshi
  • Funabashi, Toshihisa

Abstract

Probabilistic paradigms for wind turbine controller design have been gaining attention. Motivation derives from the need to replace outdated empirical-based designs with more physically relevant models. This paper proposes an adaptive controller in the form of a linear quadratic Gaussian (LQG) for control of a stall-regulated, variable speed wind turbine generator (WTG). In the control scheme, the strategy is twofold: maximization of energy captured from the wind and minimization of the damage caused by mechanical fatigue due to variation of torque peaks generated by wind gusts. Estimated aerodynamic torque and rotational speed are used to determine the most favorable control strategy to stabilize the plant at all operating points (OPs). The performance of the proposed controller is compared with the classical proportional-integral-derivative (PID) controller. The LQG is seen to be significantly more efficient especially in the alleviation of high aerodynamic torque variations and hence mechanical stresses on the plant drive train. Simulation results validate the effectiveness of the proposed method.

Suggested Citation

  • Billy Muhando, Endusa & Senjyu, Tomonobu & Urasaki, Naomitsu & Yona, Atsushi & Kinjo, Hiroshi & Funabashi, Toshihisa, 2007. "Gain scheduling control of variable speed WTG under widely varying turbulence loading," Renewable Energy, Elsevier, vol. 32(14), pages 2407-2423.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:14:p:2407-2423
    DOI: 10.1016/j.renene.2006.12.011
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    References listed on IDEAS

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    1. Senjyu, Tomonobu & Tamaki, Satoshi & Muhando, Endusa & Urasaki, Naomitsu & Kinjo, Hiroshi & Funabashi, Toshihisa & Fujita, Hideki & Sekine, Hideomi, 2006. "Wind velocity and rotor position sensorless maximum power point tracking control for wind generation system," Renewable Energy, Elsevier, vol. 31(11), pages 1764-1775.
    2. Hansen, Anca D. & Sørensen, Poul & Iov, Florin & Blaabjerg, Frede, 2006. "Centralised power control of wind farm with doubly fed induction generators," Renewable Energy, Elsevier, vol. 31(7), pages 935-951.
    3. Mohamed, Amal Z. & Eskander, Mona N. & Ghali, Fadia A., 2001. "Fuzzy logic control based maximum power tracking of a wind energy system," Renewable Energy, Elsevier, vol. 23(2), pages 235-245.
    4. Bouscayrol, A. & Delarue, Ph. & Guillaud, X., 2005. "Power strategies for maximum control structure of a wind energy conversion system with a synchronous machine," Renewable Energy, Elsevier, vol. 30(15), pages 2273-2288.
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    Cited by:

    1. Jabbari Asl, Hamed & Yoon, Jungwon, 2016. "Power capture optimization of variable-speed wind turbines using an output feedback controller," Renewable Energy, Elsevier, vol. 86(C), pages 517-525.
    2. Boukettaya, Ghada & Krichen, Lotfi, 2014. "A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications," Energy, Elsevier, vol. 71(C), pages 148-159.
    3. Masmoudi, Abdelkarim & Abdelkafi, Achraf & Krichen, Lotfi, 2011. "Electric power generation based on variable speed wind turbine under load disturbance," Energy, Elsevier, vol. 36(8), pages 5016-5026.
    4. Muhando, Endusa Billy & Senjyu, Tomonobu & Kinjo, Hiroshi & Funabashi, Toshihisa, 2008. "Augmented LQG controller for enhancement of online dynamic performance for WTG system," Renewable Energy, Elsevier, vol. 33(8), pages 1942-1952.
    5. Kortabarria, Iñigo & Andreu, Jon & Martínez de Alegría, Iñigo & Jiménez, Jaime & Gárate, José Ignacio & Robles, Eider, 2014. "A novel adaptative maximum power point tracking algorithm for small wind turbines," Renewable Energy, Elsevier, vol. 63(C), pages 785-796.

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