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Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor

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  • Pan, Lin
  • Xiong, Yong
  • Zhu, Ze
  • Wang, Leichong

Abstract

This study proposes a modeling method of soft measurement of offshore wind speed using Kernal Extreme Learning Machine (KELM). The soft measurement model of offshore wind speed is presented based on the data-driven method and kernel function extreme learning machine. An improved gray Wolf optimization algorithm is applied to optimize its parameters to enhance the measurement accuracy. Finally, based on the established offshore wind speed measurement model, a feedforward and feedback variable rotor controller is designed and verified by simulation, which proves the effectiveness of the research in this study.

Suggested Citation

  • Pan, Lin & Xiong, Yong & Zhu, Ze & Wang, Leichong, 2022. "Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor," Renewable Energy, Elsevier, vol. 184(C), pages 1002-1017.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:1002-1017
    DOI: 10.1016/j.renene.2021.11.104
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    References listed on IDEAS

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    Cited by:

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    2. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.

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