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Characteristics and modelling of wake for aligned multiple turbines based on numerical simulation.

Authors :
Zhang, Runze
Xin, Zhiqiang
Huang, Guoqing
Yan, Bowen
Zhou, Xuhong
Deng, Xiaowei
Source :
Journal of Wind Engineering & Industrial Aerodynamics. Sep2022, Vol. 228, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Wind energy has become one of the most commercially prospective renewable energies. However, the wake effect of wind turbine can reduce the power generation efficiency and increase the fatigue loading of downstream turbines. Hence, the wake effect study has attracted increasing interests. Compared with the extensive study on the single turbine wake, that on the superposition effect of multiple turbine (multi-turbine) wakes is limited. In this study, the characteristics of the wake velocity and turbulence intensity are studied and the exponential superposition model is proposed for the aligned multi-turbine wakes. Firstly, Simulator for Offshore Wind Farm Applications (SOWFA), a high-fidelity simulator for the interaction between wind turbine dynamics and the flow in a wind farm, is used to analyze the distribution of aligned multi-turbine wakes. It is observed that wakes reach the steady state from second turbine in the aligned turbines. Then influences of different factors on the accuracy of existing superposition models are studied. It is found spacing has significant effect on the performance of superposition models. Furthermore, the exponential superposition model with higher applicability is proposed for the wake velocity and turbulence intensity. Finally, this model is validated by the benchmark data of real wind farms. • Characteristics of the wake velocity and turbulence intensity of aligned multiple turbines are addressed. • Influences of different factors on the accuracy of existing superposition models are addressed. • Exponential superposition model is proposed for the wake velocity and turbulence intensity. • Validations in real wind farms show exponential superposition model has good performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01676105
Volume :
228
Database :
Academic Search Index
Journal :
Journal of Wind Engineering & Industrial Aerodynamics
Publication Type :
Academic Journal
Accession number :
158727611
Full Text :
https://doi.org/10.1016/j.jweia.2022.105097