Full field assessment of wind turbine near wake deviation in relation to yaw misalignment

Trujillo, Juan José; Seifert, Janna Kristina; Würth, Ines; Schlipf, David; Kühn, Martin

doi:10.5194/wes-2015-5

Cited by 3 publications

(3 citation statements)

References 12 publications

(25 reference statements)

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“…This is expected due to a lateral thrust component of the rotor as a result of yaw misalignment, which has been observed and 10 described in numerous studies including (Medici and Alfredsson, 2006;Jiménez et al, 2010;Vollmer et al, 2016;Trujillo et al, 2016). The deflection of the velocity deficit is quantified using the approach described in Section 2.2, the results are listed in Table 2 including the resulting wake skew angles.…”

Section: Wakes During Yaw-misalignmentmentioning

confidence: 86%

Wind tunnel experiments on wind turbine wakes in yaw: Redefining the wake width

Schottler¹,

Bartl²,

Mühle³

et al. 2018

Preprint

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Abstract. This paper presents an investigation of wakes behind model wind turbines, including cases of yaw misalignment. Two different turbines were used and their wakes are compared, isolating effects of boundary conditions and turbine specifications. The results show that areas of strongly heavy-tailed distributed velocity increments are surrounding the velocity deficit in all cases examined. Thus, a wake is significantly wider when two-point statistics are included as opposed to a description limited to one-point quantities. As non-Gaussian distributions of velocity increments affect loads of downstream rotors, our findings impact the application of active wake steering through yaw misalignment as well as wind farm layout optimizations and should 10 therefore be considered in future wake studies, wind farm layout and farm control approaches. Further, the velocity deficits behind both turbines are deformed to a kidney-like curled shape during yaw misalignment, for which parameterization methods are introduced. Moreover, the lateral wake deflection during yaw misalignment is investigated.

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Section: Wakes During Yaw-misalignmentmentioning

confidence: 86%

Wind tunnel experiments on wind turbine wakes in yaw: Redefining the wake width

Schottler¹,

Bartl²,

Mühle³

et al. 2018

Preprint

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“…Since then, a wide range of scanning techniques has been developed for both ground‐ and nacelle‐based LiDAR measurements of isolated wind turbines, multiple wakes, and wake interactions . LiDAR wake measurements were performed in the presence of yaw misalignment of the turbine rotor or wind veer, which induce a departure of the wake cross‐section from roughly axisymmetric shape, even removing the skewing effect due to the vertical shear. Doppler radars have also been used to measure wakes produced by utility‐scale wind turbines .…”

Section: Introductionmentioning

confidence: 99%

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

2019

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Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind-turbine wakes for different hub-height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met-tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub-height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near-wake velocity field, while it affects noticeably the far-wake evolution and recovery. A secondary effect on wake-recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake-generated turbulence fosters wake recovery. A semi-empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models. KEYWORDSLiDAR, wake, wind farm, wind turbine INTRODUCTIONThe recent worldwide outbreak of wind power production poses new challenges for wind farm designers seeking optimal layout and control strategies to maximize profitability of wind power plants. 1,2 A considerable factor for power losses and increased fatigue loads in large wind farms is connected with wake interactions, 3-6 which are affected by farm layout, turbine settings, site topography, and are highly variable with the static stability of the atmospheric boundary layer (ABL). 7-9 Furthermore, the increasing size of wind turbine rotors 10,11 exacerbates underperformance due to wake interactions as a consequence of the increased wake extent and, in turn, the longer downstream distance required for wake recovery.Continuous improvements in remote-sensing techniques, aiming to measure wind atmospheric turbulence, have been leveraged to achieve a deeper understanding of ABL flows 12-14 and to investigate the evolution of wakes produced by utility-scale wind turbines. [15][16][17][18] One of the first campaigns performed with light detection and ranging (LiDAR) systems with the goal of measuring wind-turbine wakes took place at a site near the coast of the northern part of Germany to probe reduction of the wind speed at certain distances downstream of a wind turbine rotor. 19Since then, a wide range of scann...

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“…Lidar technology has found many successful applications within many different aspects of the field of wind energy. This includes power performance measurements, wind field characterization in simple and complex sites, atmospheric boundary layer characterization, wake measurements, correction and optimization of turbine operation, among others. Based on the amount of previous experiences with lidars, it is clear that this technology has great potential for validation of wind turbine load models where accurate wind field and turbulence inputs are required.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine load validation using lidar‐based wind retrievals

et al. 2019

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We define and demonstrate a procedure for carrying out wind turbine load validation based on measurements from nacelle‐mounted scanning lidars. Two coherent Doppler lidar systems, a pulsed lidar and a continuous‐wave lidar, are mounted on a 2.3‐MW wind turbine equipped with load measurement sensors. Wind measurements from a meteorological mast mounted at 2.5 rotor diameters distance are used as reference. The study shows how lidar measurements are processed and applied as inputs to aeroelastic load simulations, and the results are then compared with simulations where the wind inputs have been determined using the meteorological mast data in compliance with the IEC61400‐13 standard. For the majority of simulation cases considered, the use of nacelle‐mounted lidar measurements results in load estimation uncertainties lower or equal to those that are based on measurements from cup anemometers on the mast. These results demonstrate the usefulness of nacelle‐mounted lidars as tools for carrying out load validation without the need of meteorological masts.

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Full field assessment of wind turbine near wake deviation in relation to yaw misalignment

Cited by 3 publications

References 12 publications

Wind tunnel experiments on wind turbine wakes in yaw: Redefining the wake width

Wind tunnel experiments on wind turbine wakes in yaw: Redefining the wake width

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Wind turbine load validation using lidar‐based wind retrievals

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