scholarly journals Analytical model for the power-yaw sensitivity of wind turbines operating in full wake

2019 ◽  
Author(s):  
Jaime Liew ◽  
Albert M. Urbán ◽  
Søren Juhl Andersen
Keyword(s):  
Wind Turbines ◽  
Wind Field ◽  
Wind Farm ◽  
Power Production ◽  
Loss Coefficient ◽  
Wake Flow ◽  
Wind Farm Optimization ◽  
Wake Effects ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increase for turbines operating in wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated with the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the waked wind field in order to determine a power-yaw loss coefficient. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation of the incoming wind field, due to the presence of wake(s), plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power-yaw loss coefficient can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30°. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the effects of wake effects and yaw misalignment as well as uncertainty on power output.

scholarly journals Analytical model for the power–yaw sensitivity of wind turbines operating in full wake

2020 ◽  
Vol 5 (1) ◽  
pp. 427-437 ◽  
Author(s):  
Jaime Liew ◽  
Albert M. Urbán ◽  
Søren Juhl Andersen
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Wake Flow ◽  
High Fidelity ◽  
Combined Effects ◽  
Wind Farm Optimization ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations ◽  
Blade Element

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increases for turbines operating in the wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated, with a focus on the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the wake inflow in order to determine a power–yaw loss exponent. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium- and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation in the incoming wind field, due to the presence of wakes, plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power–yaw loss exponent can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30∘. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the combined effects of wakes and yaw misalignment as well as the uncertainty on power output.

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.

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>

Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

2011 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Vertical Axis ◽  
Optimal Placement ◽  
Vertical Axis Wind Turbines ◽  
Wake Effects ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

scholarly journals A Study of the Impact of Pitch Misalignment on Wind Turbine Performance

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 8 ◽  
Author(s):  
Davide Astolfi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Production ◽  
Test Case ◽  
Before And After ◽  
Input Variables ◽  
The Impact

Pitch angle control is the most common means of adjusting the torque of wind turbines. The verification of its correct function and the optimization of its control are therefore very important for improving the efficiency of wind kinetic energy conversion. On these grounds, this work is devoted to studying the impact of pitch misalignment on wind turbine power production. A test case wind farm sited onshore, featuring five multi-megawatt wind turbines, was studied. On one wind turbine on the farm, a maximum pitch imbalance between the blades of 4.5 ° was detected; therefore, there was an intervention for recalibration. Operational data were available for assessing production improvement after the intervention. Due to the non-stationary conditions to which wind turbines are subjected, this is generally a non-trivial problem. In this work, a general method was formulated for studying this kind of problem: it is based on the study, before and after the upgrade, of the residuals between the measured power output and a reliable model of the power output itself. A careful formulation of the model is therefore crucial: in this work, an automatic feature selection algorithm based on stepwise multivariate regression was adopted, and it allows identification of the most meaningful input variables for a multivariate linear model whose target is the power of the wind turbine whose pitch has been recalibrated. This method can be useful, in general, for the study of wind turbine power upgrades, which have been recently spreading in the wind energy industry, and for the monitoring of wind turbine performances. For the test case of interest, the power of the recalibrated wind turbine is modeled as a linear function of the active and reactive power of the nearby wind turbines, and it is estimated that, after the intervention, the pitch recalibration provided a 5.5% improvement in the power production below rated power. Wind turbine practitioners, in general, should pay considerable attention to the pitch imbalance, because it increases loads and affects the residue lifetime; in particular, the results of this study indicate that severe pitch misalignment can heavily impact power production.

Design Analysis of Critical Concepts Influence Wind Farm Production and Efficiency

2018 ◽  
Vol 40 ◽  
pp. 136-150
Author(s):  
Naima Charhouni ◽  
Mohammed Sallaou ◽  
Khalifa Mansouri
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Production Efficiency ◽  
Wind Farms ◽  
Power Production ◽  
Separation Distance ◽  
Design Analysis ◽  
Offshore Wind ◽  
Farm Production ◽  
Wind Farm Design

Wind farm deficiency caused by wake turbine interactions has received an important attention by scientific researchers in recent years. However the quality of power production is strongly depends on wind turbines location from others. In this regard, this paper proposes a comprehensive design analysis of crucial concepts that aid to plan for an efficient wind farm design. Indeed, the wake modeling problem is addressed in this analysis by comparing three models with available measured data gotten from literature. A configuration of wind turbines placement within the offshore wind farm as a function of separation distance is investigated in this study considering four wind farms layout. In addition to these elements, four rotor diameters size are evaluated as critical concept for wind turbine selection and production .The results obtained demonstrate that it is complicated to make a balance between three conflicted objectives related to the power production, efficiency and surface land area required for wind farm as a function of these crucial concepts.

scholarly journals Wind Farm Loads under Wake Redirection Control

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4088
Author(s):  
Stoyan Kanev ◽  
Edwin Bot ◽  
Jack Giles
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Power Gain ◽  
Main Obstacle ◽  
Depth Analysis ◽  
Wake Effects ◽  
Fatigue Loads ◽  
Positive Effect

Active wake control (AWC) is a strategy for operating wind farms in such a way as to reduce the wake effects on the wind turbines, potentially increasing the overall power production. There are two concepts to AWC: induction control and wake redirection. The former strategy boils down to down-regulating the upstream turbines in order to increase the wind speed in their wakes. This has generally a positive effect on the turbine loading. The wake redirection concept, which relies on intentional yaw misalignment to move wakes away from downstream turbines, has a much more prominent impact and may lead to increased loading. Moreover, the turbines are typically not designed and certified to operate at large yaw misalignments. Even though the potential upsides in terms of power gain are very interesting, the risk for damage or downtime due to increased loading is seen as the main obstacle preventing large scale implementation of this technology. In order to provide good understanding on the impacts of AWC on the turbine loads, this paper presents the results from an in-depth analysis of the fatigue loads on the turbines of an existing wind farm. Even though for some wind turbine components the fatigue loads do increase for some wind conditions under yaw misalignment, it is demonstrated that the wake-induced loading decreases even more so that the lifetime loads under AWC are generally lower.

Wind Farm Micro-Siting Optimization and Benefits Analysis

2014 ◽  
Vol 1055 ◽  
pp. 389-393
Author(s):  
Ze Jia Hua ◽  
Yan Zhang Gu ◽  
Chen Xuan Hou
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Economic Benefits ◽  
Stable Operation ◽  
Technical Parameters

This paper puts forward a creative turbine micro-siting scheme breaking the 3D-5D rule, and calculates wake losses, power production and its economic benefits in traditional wind farm micro-siting scheme and creative wind farm micro-siting scheme by evaluating software WEPAS and MWVE. Results show that the micro-siting scheme breaking 3D-5D is more feasible and effective. It not only ensures the safe and stable operation of the wind turbines and technical parameters, but also the power production and the initial investment have been optimized.

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.

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