scholarly journals Large Eddy Simuation of an Onshore Wind Farm with the Actuator Line Model Including Wind Turbine’s Control Below and Above Rated Wind Speed

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3508 ◽  
Author(s):  
Andrés Guggeri ◽  
Martín Draper
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Fluid Dynamic ◽  
Power Production ◽  
Model Framework ◽  
Line Model ◽  
Onshore Wind ◽  
Large Eddy ◽  
Actuator Line Model

As the size of wind turbines increases and their hub heights become higher, which partially explains the vertiginous increase of wind power worldwide in the last decade, the interaction of wind turbines with the atmospheric boundary layer (ABL) and between each other is becoming more complex. There are different approaches to model and compute the aerodynamic loads, and hence the power production, on wind turbines subject to ABL inflow conditions ranging from the classical Blade Element Momentum (BEM) method to Computational Fluid Dynamic (CFD) approaches. Also, modern multi-MW wind turbines have a torque controller and a collective pitch controller to manage power output, particularly in maximizing power production or when it is required to down-regulate their production. In this work the results of a validated numerical method, based on a Large Eddy Simulation-Actuator Line Model framework, was applied to simulate a real 7.7 MNW onshore wind farm on Uruguay under different wind conditions, and hence operational situations are shown with the aim to assess the capability of this approach to model actual wind farm dynamics. A description of the implementation of these controllers in the CFD solver Caffa3d, presenting the methodology applied to obtain the controller parameters, is included. For validation, the simulation results were compared with 1 Hz data obtained from the Supervisory Control and Data Acquisition System of the wind farm, focusing on the temporal evolution of the following variables: Wind velocity, rotor angular speed, pitch angle, and electric power. In addition to this, simulations applying active power control at the wind turbine level are presented under different de-rate signals, both constant and time-varying, and were subject to different wind speed profiles and wind directions where there was interaction between wind turbines and their wakes.

scholarly journals Research on the Power Capture and Wake Characteristics of a Wind Turbine Based on a Modified Actuator Line Model

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 282
Author(s):  
Feifei Xue ◽  
Heping Duan ◽  
Chang Xu ◽  
Xingxing Han ◽  
Yanqing Shangguan ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Output Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Three Dimensional ◽  
Wind Farms ◽  
Line Model ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Actuator Line Model

On a wind farm, the wake has an important impact on the performance of the wind turbines. For example, the wake of an upstream wind turbine affects the blade load and output power of the downstream wind turbine. In this paper, a modified actuator line model with blade tips, root loss, and an airfoil three-dimensional delayed stall was revised. This full-scale modified actuator line model with blades, nacelles, and towers, was combined with a Large Eddy Simulation, and then applied and validated based on an analysis of wind turbine wakes in wind farms. The modified actuator line model was verified using an experimental wind turbine. Subsequently, numerical simulations were conducted on two NREL 5 MW wind turbines with different staggered spacing to study the effect of the staggered spacing on the characteristics of wind turbines. The results show that the output power of the upstream turbine stabilized at 5.9 MW, and the output power of the downstream turbine increased. When the staggered spacing is R and 1.5R, both the power and thrust of the downstream turbine are severely reduced. However, the length of the peaks was significantly longer, which resulted in a long-term unstable power output. As the staggered spacing increased, the velocity in the central near wake of the downstream turbine also increased, and the recovery speed at the threshold of the wake slowed down. The modified actuator line model described herein can be used for the numerical simulation of wakes in wind farms.

scholarly journals Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

2021 ◽  
Vol 6 (6) ◽  
pp. 1427-1453
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

2021 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Aerodynamic Rotor Performance of a 3300-kW Modern Commercial Large-Scale Wind Turbine Installed in a Wind Farm

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Mehmet Bilgili ◽  
Mehmet Tontu ◽  
Besir Sahin
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farm ◽  
Thrust Force ◽  
Speed Ratio ◽  
Flow Angle ◽  
Onshore Wind ◽  
Turbine Performance

Abstract Wind turbine technology in the world has been developed by continuously improving turbine performance, design, and efficiency. Over the last 40 years, the rated capacity and dimension of the commercial wind turbines have increased dramatically, so the energy cost has declined significantly, and the industry has moved from an idealistic position to an acknowledged component of the power generation industry. For this reason, a thorough examination of the aerodynamic rotor performance of a modern large-scale wind turbine working on existing onshore wind farms is critically important to monitor and control the turbine performance and also for forecasting turbine power. This study focuses on the aerodynamic rotor performance of a 3300-kW modern commercial large-scale wind turbine operating on an existing onshore wind farm based on the measurement data. First, frequency distributions of wind speeds and directions were obtained using measurements over one year. Then, wind turbine parameters such as free-stream wind speed (U∞), far wake wind speed (UW), axial flow induction factor (a), wind turbine power coefficient (CP), tangential flow induction factor (a′), thrust force coefficient (CT), thrust force (T), tip-speed ratio (λ), and flow angle (ϕ) were calculated using the measured rotor disc wind speed (UD), atmospheric air temperature (Tatm), turbine rotational speed (Ω), and turbine power output (P) parameters. According to the results obtained, the maximum P, CP, CT, T, and Ω were calculated as approximately 3.3 MW, 0.45, 0.6, 330 kN, and 12.9 rpm, respectively, while the optimum λ, ϕ, U∞, and Ω for the maximum CP were determined as 7.5–8.5, 6–6.3°, 5–10 m/s, and 6–10 rpm, respectively. These calculated results can contribute to assessing the economic and technical feasibility of modern commercial large-scale wind turbines and supporting future developments in wind energy and turbine technology.

scholarly journals Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 41
Author(s):  
Zexia Zhang ◽  
Christian Santoni ◽  
Thomas Herges ◽  
Fotis Sotiropoulos ◽  
Ali Khosronejad
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Velocity Fields ◽  
Large Eddy Simulations ◽  
Wake Flow ◽  
Surface Model ◽  
Flow Conditions ◽  
Large Eddy ◽  
Turbine Wake

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES.

scholarly journals Validation of the dynamic wake meandering model with respect to loads and power production

2021 ◽  
Vol 6 (2) ◽  
pp. 441-460
Author(s):  
Inga Reinwardt ◽  
Levin Schilling ◽  
Dirk Steudel ◽  
Nikolay Dimitrov ◽  
Peter Dalhoff ◽  
...  
Keyword(s):  
Wind Speed ◽  
Turbulence Model ◽  
Wind Farm ◽  
Power Production ◽  
Special Focus ◽  
Lidar System ◽  
Onshore Wind ◽  
Model Based ◽  
Lidar Measurements ◽  
Wake Model

Abstract. The outlined analysis validates the dynamic wake meandering (DWM) model based on loads and power production measured at an onshore wind farm with small turbine distances. Special focus is given to the performance of a version of the DWM model that was previously recalibrated at the site. The recalibration is based on measurements from a turbine nacelle-mounted lidar system. The different versions of the DWM model are compared to the commonly used Frandsen wake-added turbulence model. The results of the recalibrated wake model agree very well with the measurements, whereas the Frandsen model overestimates the loads drastically for short turbine distances. Furthermore, lidar measurements of the wind speed deficit as well as the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.

A study of the impact of very large wind farms on regional weather using the WRF model in high resolution

2020 ◽  
Author(s):  
Simon Jacobsen ◽  
Aksel Walløe Hansen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wrf Model ◽  
Wind Farms ◽  
Real Data ◽  
Power Production ◽  
Wake Effects ◽  
The Impact ◽  
Large Wind

<p>The Weather Research and Forecasting (WRF) model fitted with the Fitch et al. (2012) scheme for parameterization of the effect of wind energy extraction is used to study the effects of very large wind farms on regional weather. Two real data cases have been run in a high spatial resolution (grid size 500 m). Both cases are characterized by a convective westerly flow. The inner model domain covers the North Sea and Denmark. The largest windfarm consists of 200.000 wind turbines each with a capacity of 8MW. The model is run for up to 12 hours with and without the wind farm. The impact on the regional weather of these very large wind farms are studied and presented. Furthermore, the effect of horizontal spacing between wind turbines is investigated. Significant impact on the regional weather from the very large wind farms was found. Horizontal wind speed changes occur up to 3500m above the surface. The precipitation pattern is greatly affected by the very large wind farms due to the enhanced mixing in the boundary layer. Increased precipitation occurs at the front? within the wind farm, thus leaving the airmass relatively dry downstream when it reaches the Danish coast, resulting in a decrease in precipitation here compared to the control run. The formation of a small low level jet is found above the very large wind farm. Furthermore, wake effects from individual wind turbines decrease the total power production. The wind speed in the real data cases are well above the speed of maximum power production of the wind turbines. Yet most of the 200.000 wind turbines are producing only 1MW due the wake effects. A simulation run with a wind farm of 50.000 8MW wind turbines was also run. This windfarm covers the same area as the previous one, but horizontal distance between wind turbines are 1000m instead of 500m. This configuration was found to produce a similar amount of power as the 200.000 configuration. However, the atmospheric impact on regional weather is smaller but still large with 50.000 wind turbines.</p>

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