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
  • [1] Wake Effect in Wind Farm Dynamic Modelling
    Gu, Huajie
    2017 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING CONFERENCE (AUPEC), 2017,
  • [2] On the load impact of dynamic wind farm wake mixing strategies
    Frederik, Joeri A.
    van Wingerden, Jan-Willem
    RENEWABLE ENERGY, 2022, 194 : 582 - 595
  • [3] Observations of wind farm wake recovery at an operating wind farm
    Krishnamurthy, Raghavendra
    Newsom, Rob K.
    Kaul, Colleen M.
    Letizia, Stefano
    Pekour, Mikhail
    Hamilton, Nicholas
    Chand, Duli
    Flynn, Donna
    Bodini, Nicola
    Moriarty, Patrick
    WIND ENERGY SCIENCE, 2025, 10 (02) : 361 - 380
  • [4] A Spatial Autoregressive Approach for Wake Field Prediction Across a Wind Farm
    Lin, Weijiang
    Worden, Keith
    Cross, Elizabeth
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 3, 2023, : 530 - 540
  • [5] Large scale wind farm cluster wake effects and loss prediction
    Vimalakanthan, Kisorthman
    Ravishankara, Akshay Koodly
    Bot, Edwin
    Engels, Wouter
    AIAA SCITECH 2024 FORUM, 2024,
  • [6] A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines
    Shin, Jong-Hyeon
    Lee, Jong-Hwi
    Chang, Se-Myong
    ENERGIES, 2019, 12 (21)
  • [7] On Modelling Wind-Farm Wake Turbulence Autospectra and Coherence from a Database
    Schau, Kyle A.
    Gaonkar, Gopal
    Krishnan, Vaishakh
    ENERGIES, 2019, 12 (01):
  • [8] Wind Farm Energy Efficiency Improvement and Wind Turbine Load Suppression Control Independent of Wake Model
    Yao, Qi
    Liang, Zemin
    Hu, Yang
    Fang, Fang
    Liu, Jizhen
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2025, 45 (04): : 1488 - 1500
  • [9] 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
  • [10] A momentum-conserving wake superposition method for wind farm power prediction
    Zong, Haohua
    Porte-Agel, Fernando
    JOURNAL OF FLUID MECHANICS, 2020, 889