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Performance study of multi-source driving yaw system for aiding yaw control of wind turbines

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  • Dai, Juchuan
  • He, Tao
  • Li, Mimi
  • Long, Xin

Abstract

Multi-source driving (MSD) yaw system is widely used in large-scale wind turbines. However, due to the complex coupling effect of mechanical, electrical, and hydraulic, there is a knowledge gap on the dynamic process of the MSD yaw system. To fill this gap, a dynamic coupling model of the MSD yaw system is established. In this model, multi-way power source subsystems, transmission gearbox subsystems, and hydraulic brake subsystems are established, and the coupling of multi-way yaw subsystems are realized. Based on this model, different yaw angle control modes are studied, including comparison control and servo control. To further reflect the dynamic process in-service condition, the MSD yaw system model is integrated into a wind turbine model. An approximate solution for calculating yaw load is also presented. Through this study, the results show the established MSD yaw system model can work effectively. The difference of gear meshing gaps directly affects the distribution of yaw load. The dynamic response of the yaw system, including yaw position, yaw speed, and yaw acceleration, is affected by the changing amplitude and frequency of yaw load, but the large inertia of yaw system can restrain the fluctuation of yaw dynamic response to some extent.

Suggested Citation

  • Dai, Juchuan & He, Tao & Li, Mimi & Long, Xin, 2021. "Performance study of multi-source driving yaw system for aiding yaw control of wind turbines," Renewable Energy, Elsevier, vol. 163(C), pages 154-171.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:154-171
    DOI: 10.1016/j.renene.2020.08.065
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    as
    1. Wenting Chen & Hang Liu & Yonggang Lin & Wei Li & Yong Sun & Di Zhang, 2020. "LSTM-NN Yaw Control of Wind Turbines Based on Upstream Wind Information," Energies, MDPI, vol. 13(6), pages 1-23, March.
    2. Qiu, Yong-Xing & Wang, Xiao-Dong & Kang, Shun & Zhao, Ming & Liang, Jun-Yu, 2014. "Predictions of unsteady HAWT aerodynamics in yawing and pitching using the free vortex method," Renewable Energy, Elsevier, vol. 70(C), pages 93-106.
    3. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions," Applied Energy, Elsevier, vol. 242(C), pages 1383-1395.
    4. Eriksson, Sandra & Bernhoff, Hans & Leijon, Mats, 2008. "Evaluation of different turbine concepts for wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(5), pages 1419-1434, June.
    5. Ponta, Fernando L. & Otero, Alejandro D. & Lago, Lucas I. & Rajan, Anurag, 2016. "Effects of rotor deformation in wind-turbine performance: The Dynamic Rotor Deformation Blade Element Momentum model (DRD–BEM)," Renewable Energy, Elsevier, vol. 92(C), pages 157-170.
    6. Ke, Shitang & Yu, Wenlin & Wang, Tongguang & Ge, Yaojun, 2019. "Aerodynamic performance and wind-induced effect of large-scale wind turbine system under yaw and wind-rain combination action," Renewable Energy, Elsevier, vol. 136(C), pages 235-253.
    7. Dai, J.C. & Hu, Y.P. & Liu, D.S. & Long, X., 2011. "Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model," Renewable Energy, Elsevier, vol. 36(3), pages 1095-1104.
    8. Yan Pei & Zheng Qian & Bo Jing & Dahai Kang & Lizhong Zhang, 2018. "Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection," Energies, MDPI, vol. 11(3), pages 1-14, March.
    9. Dai, Juchuan & Yang, Xin & Wen, Li, 2018. "Development of wind power industry in China: A comprehensive assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 156-164.
    10. Kanev, Stoyan, 2020. "Dynamic wake steering and its impact on wind farm power production and yaw actuator duty," Renewable Energy, Elsevier, vol. 146(C), pages 9-15.
    11. Dai, Juchuan & Yang, Xin & Hu, Wei & Wen, Li & Tan, Yayi, 2018. "Effect investigation of yaw on wind turbine performance based on SCADA data," Energy, Elsevier, vol. 149(C), pages 684-696.
    12. Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
    13. Govind, Bala, 2017. "Increasing the operational capability of a horizontal axis wind turbine by its integration with a vertical axis wind turbine," Applied Energy, Elsevier, vol. 199(C), pages 479-494.
    14. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
    15. Lanzafame, R. & Messina, M., 2007. "Fluid dynamics wind turbine design: Critical analysis, optimization and application of BEM theory," Renewable Energy, Elsevier, vol. 32(14), pages 2291-2305.
    16. Zheng, Chong Wei & Li, Chong Yin & Pan, Jing & Liu, Ming Yang & Xia, Lin Lin, 2016. "An overview of global ocean wind energy resource evaluations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1240-1251.
    17. Saleem, Arslan & Kim, Man-Hoe, 2019. "Performance of buoyant shell horizontal axis wind turbine under fluctuating yaw angles," Energy, Elsevier, vol. 169(C), pages 79-91.
    18. Sliz-Szkliniarz, B. & Eberbach, J. & Hoffmann, B. & Fortin, M., 2019. "Assessing the cost of onshore wind development scenarios: Modelling of spatial and temporal distribution of wind power for the case of Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 514-531.
    19. Jeong, Min-Soo & Kim, Sang-Woo & Lee, In & Yoo, Seung-Jae & Park, K.C., 2013. "The impact of yaw error on aeroelastic characteristics of a horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 60(C), pages 256-268.
    20. Shuting Wan & Lifeng Cheng & Xiaoling Sheng, 2015. "Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model," Energies, MDPI, vol. 8(7), pages 1-16, June.
    21. Kress, C. & Chokani, N. & Abhari, R.S., 2015. "Downwind wind turbine yaw stability and performance," Renewable Energy, Elsevier, vol. 83(C), pages 1157-1165.
    22. Lopez, Daniel & Kuo, Jim & Li, Ni, 2019. "A novel wake model for yawed wind turbines," Energy, Elsevier, vol. 178(C), pages 158-167.
    23. Uzunoglu, E. & Guedes Soares, C., 2019. "Yaw motion of floating wind turbine platforms induced by pitch actuator fault in storm conditions," Renewable Energy, Elsevier, vol. 134(C), pages 1056-1070.
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