scholarly journals Experimental investigation of wind turbine wake and load dynamics during yaw manoeuvres

2020 ◽  
Author(s):  
Stefano Macrí ◽  
Sandrine Aubrun ◽  
Annie Leroy ◽  
Nicolas Girard
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Temporal Dynamics ◽  
Wake Flow ◽  
Aerodynamic Load ◽  
Flow Conditions ◽  
Steering Control ◽  
Load Variation ◽  
Wake Interactions ◽  
Particle Imaging

Abstract. This paper investigates the effect of yawing a wind turbine on its wake deviation dynamics and on the global load variation of a downstream wind turbine during a positive and negative yaw manoeuvre, representing a misalignment/realignment scenario. Yaw manoeuvres could be used to voluntarily misalign wind turbines when wake steering control is targeted. The aim of this wind farm control strategy, which is increasingly studied, is to optimize the overall production of the wind farm and possibly its lifetime, by mitigating wake interactions. Whereas wake flow and wind turbine load dynamics during yaw manoeuvres are usually approached by quasi-static models, the present study aims at quantifying dynamical properties of these phenomena. Wind tunnel experiments were conducted in three different configurations, varying both scaling and flow conditions, in which the yaw manoeuvre was reproduced in a homogeneous turbulent flow at two different scales, and in a more realistic flow such as a modelled atmospheric boundary layer. The effects of yaw control on the wake deviation were investigated by the use of stereo Particle Imaging Velocimetry while the load variation on a downstream wind turbine was measured through an unsteady aerodynamic load balance. While overall results show a non dependence of the wake and load dynamics on the flow conditions and Reynolds scales, they highlight an influence of the yaw manoeuvre direction on their temporal dynamics.

scholarly journals Experimental investigation of wind turbine wake and load dynamics during yaw maneuvers

2021 ◽  
Vol 6 (2) ◽  
pp. 585-599
Author(s):  
Stefano Macrí ◽  
Sandrine Aubrun ◽  
Annie Leroy ◽  
Nicolas Girard
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Temporal Dynamics ◽  
Wake Flow ◽  
Aerodynamic Load ◽  
Flow Conditions ◽  
Steering Control ◽  
Load Variation ◽  
Wake Interactions ◽  
Particle Imaging

Abstract. This article investigates the far wake response of a yawing upstream wind turbine and its impact on the global load variation in a downstream wind turbine. In order to represent misalignment and realignment scenarios, the upstream wind turbine was subjected to positive and negative yaw maneuvers. Yaw maneuvers could be used to voluntarily misalign wind turbines when wake steering control is targeted. The aim of this wind farm control strategy is to optimize the overall production of the wind farm and possibly its lifetime, by mitigating wake interactions. While wake flow and wind turbine load modifications during yaw maneuvers are usually described by quasi-static approaches, the present study aims at quantifying the main transient characteristics of these phenomena. Wind tunnel experiments were conducted in three different configurations, varying both scaling and flow conditions, in which the yaw maneuver was reproduced in a homogeneous turbulent flow at two different scales and in a more realistic flow such as a modeled atmospheric boundary layer. The effects of yaw control on the wake deviation were investigated by the use of stereo particle imaging velocimetry while the load variation on a downstream wind turbine was measured through an unsteady aerodynamic load balance. While overall results show a nondependence of the wake and load dynamics on the flow conditions and Reynolds scales, they highlight an influence of the yaw maneuver direction on their temporal dynamics.

scholarly journals Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

2021 ◽  
Vol 6 (4) ◽  
pp. 997-1014
Author(s):  
Janna Kristina Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Flow Conditions ◽  
Output Fluctuations ◽  
Power Difference ◽  
Time Correlations

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

Simulation of Wake Interactions in Wind Farms Using an Immersed Wind Turbine Model

2013 ◽  
Vol 136 (6) ◽  
Author(s):  
S. Jafari ◽  
N. Chokani ◽  
R. S. Abhari
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Navier Stokes ◽  
The Novel ◽  
Wind Tunnel Experiments ◽  
Wake Interactions ◽  
Turbine Model

The accurate modeling of the wind turbine wakes in complex terrain is required to accurately predict wake losses. In order to facilitate the routine use of computational fluid dynamics in the optimized micrositing of wind turbines within wind farms, an immersed wind turbine model is developed. This model is formulated to require grid resolutions that are comparable to that in microscale wind simulations. The model in connection with the k-ω turbulence model is embedded in a Reynolds-averaged Navier–Stokes solver. The predictions of the model are compared to available wind tunnel experiments and to measurements at the full-scale Sexbierum wind farm. The good agreement between the predictions and measurements demonstrates that the novel immersed turbine model is suited for the optimized micrositing of wind turbines in complex terrain.

scholarly journals Quantification of Preferential Contribution of Reynolds Shear Stresses and Flux of Mean Kinetic Energy Via Conditional Sampling in a Wind Turbine Array

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hawwa Falih Kadum ◽  
Devin Knowles ◽  
Raúl Bayoán Cal
Keyword(s):  
Shear Stress ◽  
Kinetic Energy ◽  
Wind Turbine ◽  
Momentum Flux ◽  
Wind Farm ◽  
Reynolds Shear Stress ◽  
Stress Component ◽  
Wake Flow ◽  
Shear Stresses ◽  
Balanced Distribution

Conditional statistics are employed in analyzing wake recovery and Reynolds shear stress (RSS) and flux directional out of plane component preference. Examination of vertical kinetic energy entrainment through describing and quantifying the aforementioned quantities has implications on wind farm spacing, design, and power production, and also on detecting loading variation due to turbulence. Stereographic particle image velocimetry measurements of incoming and wake flow fields are taken for a 3 × 4 model wind turbine array in a scaled wind tunnel experiment. Reynolds shear stress component is influenced by ⟨uv⟩ component, whereas ⟨vw⟩ is more influenced by streamwise advection of the flow; u, v, and w being streamwise, vertical, and spanwise velocity fluctuations, respectively. Relative comparison between sweep and ejection events, ΔS⟨uiuj⟩, shows the role of streamwise advection of momentum on RSS values and direction. It also shows their tendency to an overall balanced distribution. ⟨uw⟩ intensities are associated with ejection elevated regions in the inflow, yet in the wake, ⟨uw⟩ is linked with sweep dominance regions. Downward momentum flux occupies the region between hub height and top tip. Sweep events contribution to downward momentum flux is marginally greater than ejection events'. When integrated over the swept area, sweeps contribute 55% of the net downward kinetic energy flux and 45% is the ejection events contribution. Sweep dominance is related to momentum deficit as its value in near wake elevates 30% compared to inflow. Understanding these quantities can lead to improved closure models.

A Comparative Study of the Wind Loads and Wake Characteristics of a Single-Rotor Wind Turbine and Dual-Rotor Wind Turbines

2014 ◽  
Author(s):  
Ahmet Ozbay ◽  
Wei Tian ◽  
Hui Hu
Keyword(s):  
Turbulent Flow ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farm ◽  
Wake Flow ◽  
Wind Loads ◽  
Neutral Stability ◽  
Near Wake ◽  
Wake Characteristics

An experimental study was carried out to investigate the aeromechanics and wake characteristics of dual-rotor wind turbines (DRWTs) with co- and counter-rotating configurations, in comparison to those of a conventional singlerotor wind turbine (SRWT), in order to elucidate the underlying physics to explore/optimize design of wind turbines for higher power yield and better durability. The experiments were performed in a large-scale Aerodynamic/Atmospheric Boundary Layer (AABL) wind tunnel under neutral stability conditions. In addition to measuring the power output performance of DRWT and SRWT systems, static and dynamic wind loads acting on those systems were also investigated. Furthermore, a high resolution PIV system was used for detailed near wake flow field measurements (free-run and phase-locked) so as to quantify the near wake turbulent flow structures and observe the transient behavior of the unsteady vortex structures in the wake of DRWT and SRWT systems. In the light of the promising experimental results on DRWTs, this study can be extended further to investigate the turbulent flow in the far wake of DRWTs and utilize multiple DRWTs in different wind farm operations.

Simulation of Wake Interactions in Wind Farms Using an Immersed Wind Turbine Model

2013 ◽  
Author(s):  
S. Jafari ◽  
N. Chokani ◽  
R. S. Abhari
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Navier Stokes ◽  
The Novel ◽  
Wind Tunnel Experiments ◽  
Wake Interactions ◽  
Turbine Model

The accurate modelling of the wind turbine wakes in complex terrain is required to accurately predict wake losses. In order to facilitate the routine use of computational fluid dynamics in the optimised micrositing of wind turbines within wind farms, an immersed wind turbine model is developed. This model is formulated to require grid resolutions that are comparable to that in microscale wind simulations. The model in connection with the k-ω turbulence model is embedded in a Reynolds-Averaged Navier Stokes solver. The predictions of the model are compared to available wind tunnel experiments and to measurements at the full-scale Sexbierum wind farm. The good agreement between the predictions and measurements demonstrates that the novel immersed turbine model is suited for the optimised micrositing of wind turbines in complex terrain.

scholarly journals Development of a Computational System to Improve Wind Farm Layout, Part II: Wind Turbine Wakes Interaction

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1328 ◽  
Author(s):  
Rafael Rodrigues ◽  
Corinne Lengsfeld
Keyword(s):  
Wind Turbine ◽  
Pitch Angle ◽  
Wind Farm ◽  
Wake Flow ◽  
Design Parameters ◽  
Speed Ratio ◽  
Near Wake ◽  
Wind Farm Layout ◽  
Wake Effects ◽  
Far Wake

The second part of this work describes a wind turbine Computational Fluid Dynamics (CFD) simulation capable of modeling wake effects. The work is intended to establish a computational framework from which to investigate wind farm layout. Following the first part of this work that described the near wake flow field, the physical domain of the validated model in the near wake was adapted and extended to include the far wake. Additionally, the numerical approach implemented allowed to efficiently model the effects of the wake interaction between rows in a wind farm with reduced computational costs. The influence of some wind farm design parameters on the wake development was assessed: Tip Speed Ratio (TSR), free-stream velocity, and pitch angle. The results showed that the velocity and turbulence intensity profiles in the far wake are dependent on the TSR. The wake profile did not present significant sensitivity to the pitch angle for values kept close to the designed condition. The capability of the proposed CFD model showed to be consistent when compared with field data and kinematical models results, presenting similar ranges of wake deficit. In conclusion, the computational models proposed in this work can be used to improve wind farm layout considering wake effects.

scholarly journals Measurement procedures for characterization of wind turbine wakes with scanning Doppler wind LiDARs

2013 ◽  
Vol 10 (1) ◽  
pp. 71-75 ◽  
Author(s):  
G. V. Iungo ◽  
F. Porté-Agel
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Wake Flow ◽  
Lidar Measurement ◽  
Wake Turbulence ◽  
Main Challenge ◽  
Measuring Techniques ◽  
The Mean ◽  
Yaw Angle

Abstract. The wake flow produced from an Enercon E-70 wind turbine is investigated through three scanning Doppler wind LiDARs. One LiDAR is deployed upwind to characterize the incoming wind, while the other two LiDARs are located downstream to carry out wake measurements. The main challenge in performing measurements of wind turbine wakes is represented by the varying wind conditions, and by the consequent adjustments of the turbine yaw angle needed to maximize power production. Consequently, taking into account possible variations of the relative position between the LiDAR measurement volume and wake location, different measuring techniques were carried out in order to perform 2-D and 3-D characterizations of the mean wake velocity field. However, larger measurement volumes and higher spatial resolution require longer sampling periods; thus, to investigate wake turbulence tests were also performed by staring the LiDAR laser beam over fixed directions and with the maximum sampling frequency. The characterization of the wake recovery along the downwind direction is performed. Moreover, wake turbulence peaks are detected at turbine top-tip height, which can represent increased fatigue loads for downstream wind turbines within a wind farm.

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.

Multi-Scale Measurements in Wind Farms in Complex and Flat Terrain

2015 ◽  
Author(s):  
M. Zendehbad ◽  
N. Chokani ◽  
R. S. Abhari
Keyword(s):  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wake Flow ◽  
Small Scale ◽  
Energy Yield ◽  
Lidar System ◽  
Flow Measurements ◽  
Flat Terrain

Large-scale (that is over the kilometre scale of a whole wind farm) and small-scale (over the metre scale of an individual wind turbine) wind flow measurements are made with a mobile-based LIDAR system. The large-scale measurements detail the multiple wakes in the wind farm, including both single and double wakes, that result in up to 70% less power generation for a given wind direction, and 2.5% loss of the wind farm’s annual energy yield. The small-scale measurements show that there is a non-uniform work extraction of the turbine across the vertical extent of the wind turbine rotor. This non-uniform work extraction is accompanied by an upward pitching of the flow that is as much as 50° immediately downstream of the turbine and reduces to 10° two diameters downstream. Measurements with the mobile-based LIDAR system are made in both complex and flat terrains. A comparison of the wake profiles show that whereas in complex terrain the profiles are self-similar up to two-and-half rotor diameters downstream, this is not the case in flat terrain. It is shown that these measurements, which are made at the full-scale Reynolds number in the field, may be useful to support the development of wake flow prediction tools.

Sign in / Sign up

Export Citation Format

Share Document