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Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions

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  • Oh, Ki-Yong
  • Park, Joon-Young
  • Lee, Jun-Shin
  • Lee, JaeKyung

Abstract

As the capacity of wind turbines has increased, the loads on crucial components such as a gearbox, a generator, and blades are significantly increasing. An intelligent online monitoring system is indispensable to protect the excessive load on core components and manage a wind farm efficiently. In order to verify new online monitoring and diagnostic methods for such a monitoring system in advance, a wind turbine simulator is essential. For this purpose, we developed a simulator that has similar dynamics to an actual 3 MW wind turbine, and is thereby able to acquire a state of operation that closely resembles that of the 3 MW wind turbine under a variety of wind conditions.

Suggested Citation

  • Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    • Handle: RePEc:eee:renene:v:79:y:2015:i:c:p:150-160
    DOI: 10.1016/j.renene.2014.10.002
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    References listed on IDEAS

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    3. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    4. Fathabadi, Hassan, 2017. "Novel grid-connected solar/wind powered electric vehicle charging station with vehicle-to-grid technology," Energy, Elsevier, vol. 132(C), pages 1-11.
    5. Chan Roh, 2022. "Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators," Energies, MDPI, vol. 15(9), pages 1-18, April.
    6. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    7. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
    8. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
    9. Xuan Chau Le & Minh Quan Duong & Kim Hung Le, 2022. "Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems," Energies, MDPI, vol. 16(1), pages 1-25, December.
    10. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    11. Lim, Chae Wook, 2019. "A demonstration on the similarity of pitch response between MW wind turbine and small-scale simulator," Renewable Energy, Elsevier, vol. 144(C), pages 68-76.
    12. Maheshwari, Zeel & Kengne, Kamgang & Bhat, Omkar, 2023. "A comprehensive review on wind turbine emulators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    13. 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.
    14. Fathabadi, Hassan, 2016. "Maximum mechanical power extraction from wind turbines using novel proposed high accuracy single-sensor-based maximum power point tracking technique," Energy, Elsevier, vol. 113(C), pages 1219-1230.

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