Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements

Gao, Xiaoxia; Chen, Yao; Xu, Shinai; Gao, Wei; Xiao-hong, Zhu; Sun, Haiying; Yang, Hongxing; Han, Zhonghe; Wang, Yu; Lu, Hao

doi:10.1016/j.apenergy.2021.118182

Cited by 18 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such models are based on the Gaussian or the cosine distribution functions and provide a more accurate wind velocity distribution compared to the top‐hat model (Figure 1). Katic et al 2 also presumed that, first, the initial wake diameter was equal to the rotor diameter, and second, the wake decay (

k_{w}

) was constant, which the latter is not consistent with the measurements in wind farms 40 . One approach adopted in the analytical models developed in the later years has been to calculate a revised wake decay (

k_{w, rev}

) using the expression

k_{w, rev} = k_{w} \frac{I_{wake}}{I_{\infty}}

, where

I_{\infty}

stands for the incoming wind turbulence intensity and

I_{italicwake}

is the wake turbulence intensity 20,22,23,41 .…”

Section: Introductionmentioning

confidence: 96%

“…Katic et al 2 also presumed that, first, the initial wake diameter was equal to the rotor diameter, and second, the wake decay (k w ) was constant, which the latter is not consistent with the measurements in wind farms. 40 One approach adopted in the analytical models developed in the later years has been to calculate a revised wake decay (k w,rev ) using the expression k w,rev ¼ k w I wake I∞ , where I ∞ stands for the incoming wind turbulence intensity and I wake is the wake turbulence intensity. 20,22,23,41 For the employment of this equation, expressions of I wake as functions of I ∞ , x, wind turbine diameter (d t ), induction factor (a), and thrust coefficient (C T ) specific to the turbine are used, which are obtained through the adoption of experimental, 42 combined experimental and numerical 8 and engineering 19,23 approaches.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines

Ayoubi

Yazdi

Harsini

2022

Env Prog and Sustain Energy

View full text Add to dashboard Cite

A 2D analytical model has been proposed for predicting the wind velocity distribution inside the wake by coupling the Jensen wake model and the Gaussian function using the conservation of mass and momentum. More realistic approaches were adopted in choosing the control volume and calculation of the wake initial radius at the formation and a new modified expression was employed for the wake turbulence intensity to be used for computation of the revised wake decay. Validation of the model was performed using a body of experimental and numerical data, where the model was thoroughly compared with five analytical models of the literature. The model was successful in predicting double-interacting wakes, and gave more accurate single wake velocity profiles at the far wake region, especially at downwind distances of x ≥ 7d t (d t is the rotor diameter), which are the economically-and power harvestingoptimized spacing between the turbines in wind farms.

show abstract

k_{w}

k_{w, rev}

) using the expression

k_{w, rev} = k_{w} \frac{I_{wake}}{I_{\infty}}

, where

I_{\infty}

stands for the incoming wind turbulence intensity and

I_{italicwake}

is the wake turbulence intensity 20,22,23,41 .…”

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines

Ayoubi

Yazdi

Harsini

2022

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…Facing the difficulty in obtaining accurate previewed wind speed information with common measurements, the development of wind lidar measurement technology has promised a solution. Lidar is capable of proactively measuring wind speed within a certain range in front of the wind turbine, independent of the influence of aerodynamic shape and wake [12], previewing wind information in advance [13]. Since lidar can provide multi-point and multi-plane measurements with high accuracy, its measurement information could be used by the intelligent predictive control, improving the control performance of wind turbines.…”

Section: Introductionmentioning

confidence: 99%

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Shi,

Liu,

Deng

et al. 2023

Sensors

View full text Add to dashboard Cite

Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD–GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD–GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.

show abstract

“…Wu et al [20][21][22][23] carried out field tests on different topography and surface roughness by using a Pulse Coherent Doppler LiDAR (PCDL), revealing the loss of wind speed along the longitudinal dimension, wake size, turbulent energy dissipation rate, and its influence on the wake length of the unit. Gao et al [24] carried out comparative experimental measurements of the wind turbine wake in three wind farm regions with different complexity by laser Doppler LiDAR and discussed three wake interaction conditions of separate, full, and half wakes, respectively. Hou et al [25] studied wake turbulence characteristics of the wind turbine using the large eddy simulation method.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis

Niu

Yang

Wang³

2023

Energies

View full text Add to dashboard Cite

Taking a wind farm in the Qinghai–Tibet Plateau as the experimental site, the ZephiR Dual Mode (ZDM) LiDAR and ground-based laser LiDAR were used to scan the incoming flow and wake of the wind turbine separately. Based on wavelet analysis, the experimental study was conducted on the influence of different incoming wind speeds on the power and wake of the wind turbine. It is found that the incoming wind speeds have a great influence on the wind turbine power, and the fluctuation frequency of the wind speed is obviously higher than that of the power, that is, the scale effects of turbulence are magnified. The rotation of the wind wheel can accelerate the collapse of the large-scale turbulent structures of the incoming flow, and large-scale vortices continue to collapse into small-scale vortices, that is, the energy cascade evolution occurs. And in the wake diffusion process, the dissipation degree of the upper blade tip vortex is greater than that of the lower blade tip vortex caused by the rotation of the wind turbine. Under the same incoming flow conditions, due to the influence of tower and ground turbulence structure, the energy level connection phenomenon of the measuring points below the hub height is stronger than that above the hub height, and it weakens with the increase of the measuring distance. That is, the energy cascade of the measuring points below the hub height at 1.5 D (D is the diameter of the wind wheel) of the wake is weaker than that at 1 D of the wake. With the increase of the measuring distance of the wake, the influx of the external flow field further aggravates the momentum exchange and energy transport between the vortex clusters, that is, the influence of the external flow field gradually increases in the wake vortex pulsation.

show abstract

Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements

Cited by 18 publications

References 37 publications

A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines

A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Experimental Study on the Influence of Incoming Flow on Wind Turbine Power and Wake Based on Wavelet Analysis

Contact Info

Product

Resources

About