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Augmented LQG controller for enhancement of online dynamic performance for WTG system

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  • Muhando, Endusa Billy
  • Senjyu, Tomonobu
  • Kinjo, Hiroshi
  • Funabashi, Toshihisa

Abstract

Operation of variable speed wind turbine generator (WTG) in the above-rated region characterized by high turbulence intensities demands a trade-off between two performance metrics: maximization of energy harvested from the wind and minimization of damage caused by mechanical fatigue. This paper presents a learning adaptive controller for output power leveling and decrementing cyclic loads on the drive train. The proposed controller incorporates a linear quadratic Gaussian (LQG) augmented by a neurocontroller (NC) and regulates rotational speed by specifying the demanded generator torque. Pitch control ensures rated power output. A second-order model and a stochastic wind field model are used in the analysis. The LQG is used as a basis upon which the performance of the proposed paradigm in the trade-off studies is assessed. Simulation results indicate the proposed control scheme effectively harmonizes the relation between rotor speed and the highly turbulent wind speed thereby regulating shaft moments and maintaining rated power.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:33:y:2008:i:8:p:1942-1952
    DOI: 10.1016/j.renene.2007.12.001
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    References listed on IDEAS

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    1. 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.
    2. Maalawi, K.Y. & Badr, M.A, 2003. "A practical approach for selecting optimum wind rotors," Renewable Energy, Elsevier, vol. 28(5), pages 803-822.
    3. Flores, P. & Tapia, A. & Tapia, G., 2005. "Application of a control algorithm for wind speed prediction and active power generation," Renewable Energy, Elsevier, vol. 30(4), pages 523-536.
    4. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
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    1. Peng, Chao & Zou, Jianxiao & Li, Yan & Xu, Hongbing & Li, Liying, 2017. "A novel composite calculation model for power coefficient and flapping moment coefficient of wind turbine," Energy, Elsevier, vol. 126(C), pages 821-829.
    2. Seixas, M. & Melício, R. & Mendes, V.M.F. & Couto, C., 2016. "Blade pitch control malfunction simulation in a wind energy conversion system with MPC five-level converter," Renewable Energy, Elsevier, vol. 89(C), pages 339-350.

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