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The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake

Author

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  • Jennifer Marie Rinker

    (Department of Wind Energy, Technical University of Denmark, Anker Engelunds Vej 1 Bygning 101A, 2800 Kongens Lyngby, Denmark
    These authors contributed equally to this work.)

  • Esperanza Soto Sagredo

    (Department of Wind Energy, Technical University of Denmark, Anker Engelunds Vej 1 Bygning 101A, 2800 Kongens Lyngby, Denmark
    These authors contributed equally to this work.)

  • Leonardo Bergami

    (Conceptual Design, LM Wind Power, Jupitervej, 6000 Kolding, Denmark)

Abstract

Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the spatial variation of turbulence statistics is a tempting alternative, but the accuracy of these models with respect to the aeroelastic response is not well understood. This paper therefore highlights the effect of replacing wake meandering with spatially varying statistics (“profile functions”) in the inflow to a downstream turbine. Profile functions at different downstream and lateral locations are extracted from a large-eddy simulation with an upstream turbine and compared with two lower-fidelity models: one that prescribes both the mean and standard deviation of the turbulence and one that prescribes only the mean. The aeroelastic response of an NREL 5 MW wind turbine is simulated with the three different wake-model fidelities, and various quantities of interest are compared. The mean values for the power and rotor speed for the medium-and low-fidelity model match well, but the accuracy of the fatigue loads varies greatly depending on the load channel. Prescribing the profile function for the standard deviation is only beneficial for the tower-base fore-aft moment; all other DELs had similar accuracies for both the medium- and low-fidelity models. The paper concludes that blade DELs can be estimated using these simple models with some accuracy, but care should be taken with the load channels related to the shaft torsion and tower-base fore-aft bending moment.

Suggested Citation

  • Jennifer Marie Rinker & Esperanza Soto Sagredo & Leonardo Bergami, 2021. "The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake," Energies, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7313-:d:671873
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    References listed on IDEAS

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    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    3. Johnston, Barry & Foley, Aoife & Doran, John & Littler, Timothy, 2020. "Levelised cost of energy, A challenge for offshore wind," Renewable Energy, Elsevier, vol. 160(C), pages 876-885.
    4. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2017. "Wind farm layout optimization using a Gaussian-based wake model," Renewable Energy, Elsevier, vol. 107(C), pages 531-541.
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    Cited by:

    1. Yunliang Li & Zhaobin Li & Zhideng Zhou & Xiaolei Yang, 2023. "Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    2. Jirarote Buranarote & Yutaka Hara & Masaru Furukawa & Yoshifumi Jodai, 2022. "Method to Predict Outputs of Two-Dimensional VAWT Rotors by Using Wake Model Mimicking the CFD-Created Flow Field," Energies, MDPI, vol. 15(14), pages 1-29, July.

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