LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill

Gao, Xiaoxia; Li, Luqing; Zhang, Shaohai; Zhu, Xiaofan; Sun, Haiying; Yang, Hongxing; Wang, Yu; Lu, Hao

doi:10.1016/j.energy.2022.125051

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…The Coanda effect, also known as the wall effect, refers to the tendency where when the fluid encounters a convex surface, the fluid deviates from the original flow direction and flows with the convex surface [29]. The Coanda effect is caused by the sudden change in terrain, which leads to the pressure difference on both sides of the fluid.…”

Section: Coanda Effectmentioning

confidence: 99%

“…The above wake model for complex terrain does not take into account the wind shear effect, and the influence of the wind shear effect on the wake distribution cannot be ignored [26][27][28], especially in the vertical wake distribution. In order to solve this problem, Gao et al [29] introduced the Coanda effect into the wake model, considered the effect of wind shear, modified the wind speed distribution in the vertical direction, and proposed a complex terrain wake model suitable for the far wake region. However, the model does not fully describe the velocity distribution of the whole wake region, and it ignores the distribution characteristics of the near wake.…”

Section: Introductionmentioning

confidence: 99%

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Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

et al. 2022

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Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model that can predict the three-dimensional wind speed distribution of the entire wake region in the complex wind farm, taking into account the Coanda effect, wind shear effect, and wake subsidence under the Gaussian terrain. Two types of Doppler lidar were used to conduct wind field experiments, and the inflow wind profile and three-dimensional expansion of the wake downstream of the wind turbine on the Gaussian terrain were measured. The experimental results showed that the wake centerline and terrain curve showed similar variation characteristics, and the near wake profile was similar to a super-Gaussian shape (asymmetric super-Gaussian shape) under low-wind-speed conditions, while the near wake profile presented a bimodal shape (asymmetric bimodal shape) under high-wind-speed conditions. The predicted profiles of the Gaussian terrain wake model were compared with the experimental data and the three typical wake models. The comparison results showed that the newly proposed Gaussian terrain wake model fit well with the experimental data in both near wake and far wake regions, and it had better performance in predicting the wake speed of the Gaussian terrain wind farm than the other three wake models. It can effectively predict the three-dimensional velocity distribution in the whole wake region of complex terrain.

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Section: Coanda Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

et al. 2022

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“…The model gives a specific expression for the wake expansion rate without the need for extensive experimental calculations to determine some empirical parameters. Subsequently, the 3DJG model was improved by Gao et al [25][26][27] considering the velocity distribution characteristics of the near wake, topographic effects, and yawing. Xu et al 28 combined the 3DJG model and OpenFAST to investigate the effect of upstream wind turbine location on the aerodynamic performance of downstream wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al 28 combined the 3DJG model and OpenFAST to investigate the effect of upstream wind turbine location on the aerodynamic performance of downstream wind turbines. In addition, by correcting for the effect of wind shear using the same method as Gao et al, [24][25][26][27] we 29 propose a 3D polynomial-shaped wake model by considering the anisotropic expansion of the wake boundary.…”

Section: Introductionmentioning

confidence: 99%

A three‐dimensional analytical model based on Gaussian distribution for wind‐turbine wakes

Ling,

Zhao,

Liu

et al. 2023

Env Prog and Sustain Energy

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A Gaussian‐shaped, three‐dimensional (3DG‐k) wake model is proposed considering the shear wind effect. The model defines the wake radius rw to be equal to twice the Gaussian standard deviation σ and establishes a link with the expansion coefficient k of the physical wake boundary to describe the wake boundary. Garrad Hassan (GH) wind tunnel test data, Shiren wind farm measurement data, Nibe wind farm measurement data, and Vestas V80‐2MW wind turbine computational data by large eddy simulation method are used to validate the accuracy of the 3DG‐k, Jensen, 3D Gaussian (3DG), and 3D cosine (3D‐C) models. Results show that the 3DG‐k model exhibits higher accuracy for the four cases compared to other models. Due to its simplicity and universality, the model can be used for layout optimization and wake control of wind farms.

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“…Field measurements are also indispensable. In recent years, wind mast, SODAR (Sonic Detection and Ranging) and LiDAR (Light Detection and Ranging) [16,17] have been adopted in wake measurements of wind farms. Compared with wind tunnel measurements, field measurements are uncontrollable, which means that field measurements generally require a longer measurement period and have higher economic costs.…”

Section: Introductionmentioning

confidence: 99%

Study on Complex Wake Characteristics of Yawed Wind Turbine Using Actuator Line Method

Wang

Zhou

Cai

et al. 2023

JMSE

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In modern large-scale wind farms, power loss caused by the wake effect is more than 30%, and active yaw control can greatly reduce the influence of the wake effect by deflecting the wind turbine’s wake. The yawed wind turbine’s wake characteristics are complex, and a deep comprehension of a yawed turbine’s wake is necessary. The actuator line method combined with URANS (unsteady Reynold-averaged Navier–Stokes equations) is used to study the yawed wind turbine’s wake characteristics in this paper. Compared with an un-yawed wind turbine, a yawed one has two main characteristics, deflection and deformation. With an increasing yaw angle, turbine wake shows an increasing deflection. The results indicated that deflection at different height was different, the wake profile showed the biggest deflection at about the hub height, while the smallest deflection existed at the top and bottom of the yawed turbine’s wake. This can be visually demonstrated by the evolution of a kidney-shape velocity distribution at the vertical cross-section. Two-dimensional and three-dimensional presentations of velocity deficit distributions are presented in this paper. The evolution of an irregular kidney-shape distribution is discussed in this paper. It is formed by the momentum exchange caused by the counter-rotating vortex pair. The results indicated that the counter-rotating vortex pair was composed of the streamwise vortex flux brought by the tip vortex. Furthermore, when the wind turbine rotated clockwise and yawed clockwise, the negative vorticity of counter-rotating vortex first appeared in the upper left position.

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LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill

Cited by 13 publications

References 29 publications

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

A three‐dimensional analytical model based on Gaussian distribution for wind‐turbine wakes

Study on Complex Wake Characteristics of Yawed Wind Turbine Using Actuator Line Method

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