TY - JOUR
T1 - A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes
AU - He, Ruiyang
AU - Yang, Hongxing
AU - Sun, Haiying
AU - Gao, Xiaoxia
N1 - Funding Information:
The work described in this paper was supported by the Research Institute for Sustainable Urban Development (RISUD) with the account number of BBW8 and the FCE Dean Research project with the account number of ZVHL, The Hong Kong Polytechnic University.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/8/15
Y1 - 2021/8/15
N2 - The development of a more advanced three-dimensional wake model for wind power generation is presented based on a multivariate Gaussian distribution. The newly-presented model is closer to reality as it truly depends on two independent dimensions (namely horizontal and vertical directions) rather than the radius of a circle. For this reason, the general expression of wake expansion rate in each dimension is specifically developed. In addition, by taking into account the inflow wind shear effect, this current model is able to accurately capture the asymmetric distribution of the vertical wake profile. Four cases including experimental data from wind tunnels and field observations as well as high-fidelity numerical simulation are used to validate the present model. Compared with conventional models, this new model is capable of predicting the wake distribution of a single wind turbine reasonably well. The proposed model is highly simple with a low computational cost. Before applying this model, no additional numerical calculation or trial calculation is required. Wake velocity at any given spatial position can be calculated in an accurate and fast manner. Because of its accuracy, universality and low cost, the present three-dimensional wake model is able to make contributions to farm-level applications such as layout optimization and control strategies and therefore benefit the power output of wind farms.
AB - The development of a more advanced three-dimensional wake model for wind power generation is presented based on a multivariate Gaussian distribution. The newly-presented model is closer to reality as it truly depends on two independent dimensions (namely horizontal and vertical directions) rather than the radius of a circle. For this reason, the general expression of wake expansion rate in each dimension is specifically developed. In addition, by taking into account the inflow wind shear effect, this current model is able to accurately capture the asymmetric distribution of the vertical wake profile. Four cases including experimental data from wind tunnels and field observations as well as high-fidelity numerical simulation are used to validate the present model. Compared with conventional models, this new model is capable of predicting the wake distribution of a single wind turbine reasonably well. The proposed model is highly simple with a low computational cost. Before applying this model, no additional numerical calculation or trial calculation is required. Wake velocity at any given spatial position can be calculated in an accurate and fast manner. Because of its accuracy, universality and low cost, the present three-dimensional wake model is able to make contributions to farm-level applications such as layout optimization and control strategies and therefore benefit the power output of wind farms.
KW - Anisotropic wake expansion rate
KW - Multivariate Gaussian distribution
KW - Three-dimensional wake model
KW - Wind tunnel and field measurement validation
UR - http://www.scopus.com/inward/record.url?scp=85107790292&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2021.117059
DO - 10.1016/j.apenergy.2021.117059
M3 - Journal article
AN - SCOPUS:85107790292
SN - 0306-2619
VL - 296
JO - Applied Energy
JF - Applied Energy
M1 - 117059
ER -