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Fleetwide data-enabled reliability improvement of wind turbines

Author

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  • Verstraeten, Timothy
  • Nowé, Ann
  • Keller, Jonathan
  • Guo, Yi
  • Sheng, Shuangwen
  • Helsen, Jan

Abstract

Wind farms are an indispensable driver toward renewable and nonpolluting energy resources. However, as ideal sites are limited, placement in remote and challenging locations results in higher logistics costs and lower average wind speeds. Therefore, it is critical to increase the reliability of the turbines to reduce maintenance costs. Robust implementation requires a thorough understanding of the loads subject to the turbine's control. Yet, such dynamically changing multidimensional loads are uncommon with other machinery, and generally underresearched. Therefore, a multitiered approach is proposed to investigate the load spectrum occurring in wind farms. Our approach relies on both fundamental research using controllable test rigs, as well as analyses of real-world loading conditions in high-frequency supervisory control and data acquisition data. A method is introduced to detect operational zones in wind farm data and link them with load distributions. Additionally, while focused research further investigates the load spectrum, a method is proposed that continuously optimizes the farm's control protocols without the need to fully understand the loads that occur. A case of gearbox failure is investigated based on a vast body of past experiments and suspect loads are identified. Starting from this evidence on the cause and effects of dynamic loads, the potential of our methods is shown by analyzing real-world farm loading conditions on a steady-state case of wake and developing a preventive row-based control protocol for a case of cascading emergency brakes induced by a storm.

Suggested Citation

  • Verstraeten, Timothy & Nowé, Ann & Keller, Jonathan & Guo, Yi & Sheng, Shuangwen & Helsen, Jan, 2019. "Fleetwide data-enabled reliability improvement of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 428-437.
    • Handle: RePEc:eee:rensus:v:109:y:2019:i:c:p:428-437
    DOI: 10.1016/j.rser.2019.03.019
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    References listed on IDEAS

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    1. Helsen, Jan & Peeters, Pepijn & Vanslambrouck, Klaas & Vanhollebeke, Frederik & Desmet, Wim, 2014. "The dynamic behavior induced by different wind turbine gearbox suspension methods assessed by means of the flexible multibody technique," Renewable Energy, Elsevier, vol. 69(C), pages 336-346.
    2. Huang, Yu-Fong & Gan, Xing-Jia & Chiueh, Pei-Te, 2017. "Life cycle assessment and net energy analysis of offshore wind power systems," Renewable Energy, Elsevier, vol. 102(PA), pages 98-106.
    3. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
    4. Ding, Fangfang & Tian, Zhigang & Zhao, Fuqiong & Xu, Hao, 2018. "An integrated approach for wind turbine gearbox fatigue life prediction considering instantaneously varying load conditions," Renewable Energy, Elsevier, vol. 129(PA), pages 260-270.
    5. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
    6. Clark, Caitlyn E. & DuPont, Bryony, 2018. "Reliability-based design optimization in offshore renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 390-400.
    7. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    8. Helsen, J. & Devriendt, C. & Weijtjens, W. & Guillaume, P., 2016. "Experimental dynamic identification of modeshape driving wind turbine grid loss event on nacelle testrig," Renewable Energy, Elsevier, vol. 85(C), pages 259-272.
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    Cited by:

    1. Jamil, Faras & Verstraeten, Timothy & Nowé, Ann & Peeters, Cédric & Helsen, Jan, 2022. "A deep boosted transfer learning method for wind turbine gearbox fault detection," Renewable Energy, Elsevier, vol. 197(C), pages 331-341.

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