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Local wind speed estimation, with application to wake impingement detection

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  • Bottasso, C.L.
  • Cacciola, S.
  • Schreiber, J.

Abstract

Wind condition awareness is an important factor to maximize power extraction, reduce fatigue loading and increase the power quality of wind turbines and wind power plants. This paper presents a new method for wind speed estimation based on blade load measurements. Starting from the definition of a cone coefficient, which captures the collective zeroth-harmonic of the out-of-plane blade bending moment, a rotor-effective wind speed estimator is introduced. The proposed observer exhibits a performance similar to the well known torque balance estimator. However, while the latter only measures the average wind speed over the whole rotor disk, the proposed approach can also be applied locally, resulting in estimates of the wind speed in different regions of the rotor disk. In the present work, the proposed method is used to estimate the average wind speed over four rotor quadrants. The top and bottom quadrants are used for estimating the vertical shear profile, while the two lateral ones for detecting the presence of a wake shed by an upstream wind turbine. The resulting wake detector can find applicability in wind farm control, by indicating on which side of the rotor the upstream wake is impinging. The new approach is demonstrated with the help of field test data, as well as simulations performed with high-fidelity aeroservoelastic models.

Suggested Citation

  • Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    • Handle: RePEc:eee:renene:v:116:y:2018:i:pa:p:155-168
    DOI: 10.1016/j.renene.2017.09.044
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    References listed on IDEAS

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    1. Bottasso, C.L. & Riboldi, C.E.D., 2014. "Estimation of wind misalignment and vertical shear from blade loads," Renewable Energy, Elsevier, vol. 62(C), pages 293-302.
    2. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    3. Bottasso, C.L. & Riboldi, C.E.D., 2015. "Validation of a wind misalignment observer using field test data," Renewable Energy, Elsevier, vol. 74(C), pages 298-306.
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    Cited by:

    1. Kim, Kwang-Ho & Bertelè, Marta & Bottasso, Carlo L., 2023. "Wind inflow observation from load harmonics via neural networks: A simulation and field study," Renewable Energy, Elsevier, vol. 204(C), pages 300-312.
    2. Lio, Wai Hou & Larsen, Gunner Chr. & Thorsen, Gunhild R., 2021. "Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation," Renewable Energy, Elsevier, vol. 172(C), pages 1073-1086.

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