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LiDAR-enabled model predictive control of wind turbines with real-time capabilities

Author

Listed:
  • Bottasso, C.L.
  • Pizzinelli, P.
  • Riboldi, C.E.D.
  • Tasca, L.

Abstract

We consider LiDAR-enabled model-based collective pitch and torque controllers that can be implemented onboard a wind turbine in a hard real-time environment, in the sense that they can be computed efficiently on standard computer hardware and that require a fixed deterministic number of operations at each call.

Suggested Citation

  • Bottasso, C.L. & Pizzinelli, P. & Riboldi, C.E.D. & Tasca, L., 2014. "LiDAR-enabled model predictive control of wind turbines with real-time capabilities," Renewable Energy, Elsevier, vol. 71(C), pages 442-452.
  • Handle: RePEc:eee:renene:v:71:y:2014:i:c:p:442-452
    DOI: 10.1016/j.renene.2014.05.041
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    Cited by:

    1. Tang, Shengming & Li, Tiantian & Guo, Yun & Zhu, Rong & Qu, Hongya, 2022. "Correction of various environmental influences on Doppler wind lidar based on multiple linear regression model," Renewable Energy, Elsevier, vol. 184(C), pages 933-947.
    2. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    3. Wakui, Tetsuya & Nagamura, Atsushi & Yokoyama, Ryohei, 2021. "Stabilization of power output and platform motion of a floating offshore wind turbine-generator system using model predictive control based on previewed disturbances," Renewable Energy, Elsevier, vol. 173(C), pages 105-127.
    4. Jie Bao & Hong Yue, 2022. "Design and Assessment of a LIDAR-Based Model Predictive Wind Turbine Control," Energies, MDPI, vol. 15(17), pages 1-19, September.
    5. Tang, Shengming & Guo, Yun & Wang, Xu & Zhu, Rong & Tang, Jie & Zhang, Shuai, 2023. "Evaluation and impact factors of Doppler wind lidar during Super Typhoon Lekima (2019)," Renewable Energy, Elsevier, vol. 205(C), pages 305-316.
    6. Xiaojun Shen & Chongchen Zhou & Guojie Li & Xuejiao Fu & Tek Tjing Lie, 2018. "Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines," Energies, MDPI, vol. 11(7), pages 1-23, July.

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