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Stochastic Wake Modelling Based on POD Analysis

Author

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  • David Bastine

    (AG TWiSt, Institute of Physics, ForWind, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
    Fraunhofer Institute for Wind Energy Systems IWES, Am Seedeich 45, 27572 Bremerhaven, Germany)

  • Lukas Vollmer

    (AG TWiSt, Institute of Physics, ForWind, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
    Fraunhofer Institute for Wind Energy Systems IWES, Am Seedeich 45, 27572 Bremerhaven, Germany)

  • Matthias Wächter

    (AG TWiSt, Institute of Physics, ForWind, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany)

  • Joachim Peinke

    (AG TWiSt, Institute of Physics, ForWind, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
    Fraunhofer Institute for Wind Energy Systems IWES, Am Seedeich 45, 27572 Bremerhaven, Germany)

Abstract

In this work, large eddy simulation data is analysed to investigate a new stochastic modeling approach for the wake of a wind turbine. The data is generated by the large eddy simulation (LES) model PALM combined with an actuator disk with rotation representing the turbine. After applying a proper orthogonal decomposition (POD), three different stochastic models for the weighting coefficients of the POD modes are deduced resulting in three different wake models. Their performance is investigated mainly on the basis of aeroelastic simulations of a wind turbine in the wake. Three different load cases and their statistical characteristics are compared for the original LES, truncated PODs and the stochastic wake models including different numbers of POD modes. It is shown that approximately six POD modes are enough to capture the load dynamics on large temporal scales. Modeling the weighting coefficients as independent stochastic processes leads to similar load characteristics as in the case of the truncated POD. To complete this simplified wake description, we show evidence that the small-scale dynamics can be captured by adding to our model a homogeneous turbulent field. In this way, we present a procedure to derive stochastic wake models from costly computational fluid dynamics (CFD) calculations or elaborated experimental investigations. These numerically efficient models provide the added value of possible long-term studies. Depending on the aspects of interest, different minimalized models may be obtained.

Suggested Citation

  • David Bastine & Lukas Vollmer & Matthias Wächter & Joachim Peinke, 2018. "Stochastic Wake Modelling Based on POD Analysis," Energies, MDPI, vol. 11(3), pages 1-29, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:612-:d:135559
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    References listed on IDEAS

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    1. 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.
    2. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    3. Esteban Ferrer & Oliver M.F. Browne & Eusebio Valero, 2017. "Sensitivity Analysis to Control the Far-Wake Unsteadiness Behind Turbines," Energies, MDPI, vol. 10(10), pages 1-21, October.
    4. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
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    Cited by:

    1. Nassir Cassamo & Jan-Willem van Wingerden, 2020. "On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach," Energies, MDPI, vol. 13(24), pages 1-21, December.
    2. De Cillis, Giovanni & Cherubini, Stefania & Semeraro, Onofrio & Leonardi, Stefano & De Palma, Pietro, 2022. "Stability and optimal forcing analysis of a wind turbine wake: Comparison with POD," Renewable Energy, Elsevier, vol. 181(C), pages 765-785.
    3. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    4. Feng, Dachuan & Gupta, Vikrant & Li, Larry K.B. & Wan, Minping, 2024. "An improved dynamic model for wind-turbine wake flow," Energy, Elsevier, vol. 290(C).
    5. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).

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