Simplified wake modelling for wind farm load prediction

被引:1
|
作者
de Vaal, Jacobus B. [1 ,2 ]
Muskulus, Michael [1 ]
机构
[1] Norwegian Univ Sci & Technol, Inst Civil & Environm Engn, Trondheim, Norway
[2] Inst Energy Technol IFE, Wind Energy, Kjeller, Norway
来源
EERA DEEPWIND'2021 | 2021年 / 2018卷
关键词
D O I
10.1088/1742-6596/2018/1/012012
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper presents a simple numerical wind farm model, where pragmatic choices are made in the modelling of underlying physical processes, with the aim of making useful power production and wind turbine load estimates. The numerical model decomposes the wind farm, inspired by the approach of the dynamic wake meandering model (DWM), into simple sub-models for a single wake deficit (1D Gaussian), wake meandering (statistical), and wake added turbulence (eddy viscosity based). Particular attention is given to selecting a momentum conserving wake summation method, because of its critical role in coupling the influence of individual wakes. Results are presented to illustrate the influence that wake summation methods have on equilibrium velocity and power production in a row of turbines, for different inter-turbine spacing and inflow velocities. Comparisons against published data from the Lillgrund wind farm illustrate that the suggested modelling approach reproduces important trends observed in the field data.
引用
收藏
页数:10
相关论文
共 50 条
  • [11] Comparison of CFD Prediction and Actual Condition for Wake Effect on an Onshore Wind Farm
    Tumenbayar, Undarmaa
    Son, Jinhyuk
    Ko, Kyungnam
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2018, 8 (03): : 1667 - 1672
  • [12] Artificial Neural Networks based wake model for power prediction of wind farm
    Ti, Zilong
    Deng, Xiao Wei
    Zhang, Mingming
    Renewable Energy, 2021, 172 : 618 - 631
  • [13] Wind farm layout optimization with load constraints using surrogate modelling
    Riva, Riccardo
    Liew, Jaime
    Friis-Moller, Mikkel
    Dimitrov, Nikolay
    Barlas, Emre
    Rethore, Pierre-Elouan
    Berzonskis, Arvydas
    SCIENCE OF MAKING TORQUE FROM WIND (TORQUE 2020), PTS 1-5, 2020, 1618
  • [14] Quantifying and clustering the wake-induced perturbations within a wind farm for load analysis
    Lovera, A.
    Fekhari, E.
    Jezequel, B.
    Dupoiron, M.
    Guiton, M.
    Ardillon, E.
    WAKE CONFERENCE 2023, 2023, 2505
  • [15] LES verification of HAWC2Farm aeroelastic wind farm simulations with wake steering and load analysis
    Liew, Jaime
    Andersen, Soren Juhl
    Troldborg, Niels
    Gocmen, Tuhfe
    SCIENCE OF MAKING TORQUE FROM WIND, TORQUE 2022, 2022, 2265
  • [16] A wake detector for wind farm control
    Bottasso, C. L.
    Cacciola, S.
    Schreiber, J.
    WAKE CONFERENCE 2015, 2015, 625
  • [17] WAKE EFFECTS IN A LINEAR WIND FARM
    BEYER, HG
    PAHLKE, T
    SCHMIDT, W
    WALDL, HP
    DEWITT, U
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 1994, 51 (03) : 303 - 318
  • [18] Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions
    Vera-Tudela, Luis
    Kuehn, Martin
    RENEWABLE ENERGY, 2017, 107 : 352 - 360
  • [19] Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines
    Cai, Wei
    Hu, Yang
    Fang, Fang
    Yao, Lujin
    Liu, Jizhen
    APPLIED ENERGY, 2023, 339
  • [20] Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity
    Qian, Guo-Wei
    Ishihara, Takeshi
    Energy, 2021, 220