IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v33y2008i8p1942-1952.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148107003813
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2007.12.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Maalawi, K.Y. & Badr, M.A, 2003. "A practical approach for selecting optimum wind rotors," Renewable Energy, Elsevier, vol. 28(5), pages 803-822.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. MacPhee, David W. & Beyene, Asfaw, 2015. "Experimental and Fluid Structure Interaction analysis of a morphing wind turbine rotor," Energy, Elsevier, vol. 90(P1), pages 1055-1065.
    3. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    4. Fan, Zhixin & Zhu, Caichao, 2019. "The optimization and the application for the wind turbine power-wind speed curve," Renewable Energy, Elsevier, vol. 140(C), pages 52-61.
    5. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    6. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    7. Yolanda Vidal & Leonardo Acho & Ningsu Luo & Mauricio Zapateiro & Francesc Pozo, 2012. "Power Control Design for Variable-Speed Wind Turbines," Energies, MDPI, vol. 5(8), pages 1-18, August.
    8. Khaled, Mohamed & Ibrahim, Mostafa M. & Abdel Hamed, Hesham E. & AbdelGwad, Ahmed F., 2019. "Investigation of a small Horizontal–Axis wind turbine performance with and without winglet," Energy, Elsevier, vol. 187(C).
    9. Moradi, Hamed & Vossoughi, Gholamreza, 2015. "Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H∞ and PID controllers," Energy, Elsevier, vol. 90(P2), pages 1508-1521.
    10. 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.
    11. Chen, Z.J. & Stol, K.A. & Mace, B.R., 2017. "Wind turbine blade optimisation with individual pitch and trailing edge flap control," Renewable Energy, Elsevier, vol. 103(C), pages 750-765.
    12. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    13. Nikita Tomin, 2023. "Robust Reinforcement Learning-Based Multiple Inputs and Multiple Outputs Controller for Wind Turbines," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    14. Mseddi, Amina & Le Ballois, Sandrine & Aloui, Helmi & Vido, Lionel, 2019. "Robust control of a wind conversion system based on a hybrid excitation synchronous generator: A comparison between H∞ and CRONE controllers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 453-476.
    15. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    16. Yanwei Jing & Hexu Sun & Lei Zhang & Tieling Zhang, 2017. "Variable Speed Control of Wind Turbines Based on the Quasi-Continuous High-Order Sliding Mode Method," Energies, MDPI, vol. 10(10), pages 1-21, October.
    17. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    18. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    19. Chen, Jiahao & Hu, Zhiqiang & Liu, Geliang & Wan, Decheng, 2019. "Coupled aero-hydro-servo-elastic methods for floating wind turbines," Renewable Energy, Elsevier, vol. 130(C), pages 139-153.
    20. López, P. & Velo, R. & Maseda, F., 2008. "Effect of direction on wind speed estimation in complex terrain using neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2266-2272.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:33:y:2008:i:8:p:1942-1952. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.