Wind turbine wake tracking and its correlations with wind turbine monitoring sensors. Preliminary results

Aubrun, Sandrine; Garcia, E. Torres; Boquet, M.; Coupiac, Olivier; Girard, Nicolas

doi:10.1088/1742-6596/753/3/032003

Cited by 12 publications

(16 citation statements)

References 11 publications

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“…Many wake validation studies from remote sensing measurements focus on individual isolated turbines (Käsler et al, 2010;Bingöl et al, 2010;Trujillo et al, 2011;Hirth et al, 2012;Hirth and Schroeder, 2013;Aitken et al, 2014a;Aitken and Lundquist, 2014;Bastine et al, 2015;Kumer et al, 2015), with some studies that aim to reconstruct the three-dimensional structure of wind turbine wakes (Iungo et al, 2013;Banta et al, 2015). The interactions between multiple wakes must be captured in studies of large wind farms, as done by Clive et al (2011), Hirth et al (2015a, Kumer et al (2015), Wang and Barthelmie (2015), Aubrun et al (2016), andvan Dooren et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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“…19 Since then, a wide range of scanning techniques has been developed for both ground-and nacelle-based LiDAR measurements of isolated wind turbines, 20-23 multiple wakes, and wake interactions. [24][25][26][27] LiDAR wake measurements were performed in the presence of yaw misalignment of the turbine rotor 28 or wind veer, 29 which induce a departure of the wake cross-section from roughly axisymmetric shape, even removing the The peer review history for this article is available at https://publons.com/…”

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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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“…Moreover, by taking into account the influence of turbine wakes in wind resource assessment process, predictions of future power production will likely be improved [115]. Doppler LiDAR is an ideal instrument to measure turbine wakes that cover a large downstream volume [116], which has been applied to investigate wakes generated by an individual turbine as well as interactions between multiple turbine wakes [117,118].…”

Section: Turbine Wakementioning

confidence: 99%

“…In the Smart Rotors to Improve Wind Energy Efficiency and Sustainability (SMARTEOLE) project, two nacelle-mounted LIDARs were deployed to investigate wakes generated by two wind turbines [117]. It was observed that the turbine wakes were aligned with the wind direction, and the far wake of the upstream turbine merged with the wake generated by the downstream turbine.…”

Section: Turbine Wakementioning

confidence: 99%

A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

et al. 2019

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Doppler wind LiDAR (Light Detection And Ranging) makes use of the principle of optical Doppler shift between the reference and backscattered radiations to measure radial velocities at distances up to several kilometers above the ground. Such instruments promise some advantages, including its large scan volume, movability and provision of 3-dimensional wind measurements, as well as its relatively higher temporal and spatial resolution comparing with other measurement devices. In recent decades, Doppler LiDARs developed by scientific institutes and commercial companies have been well adopted in several real-life applications. Doppler LiDARs are installed in about a dozen airports to study aircraft-induced vortices and detect wind shears. In the wind energy industry, the Doppler LiDAR technique provides a promising alternative to in-situ techniques in wind energy assessment, turbine wake analysis and turbine control. Doppler LiDARs have also been applied in meteorological studies, such as observing boundary layers and tracking tropical cyclones. These applications demonstrate the capability of Doppler LiDARs for measuring backscatter coefficients and wind profiles. In addition, Doppler LiDAR measurements show considerable potential for validating and improving numerical models. It is expected that future development of the Doppler LiDAR technique and data processing algorithms will provide accurate measurements with high spatial and temporal resolutions under different environmental conditions.

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Wind turbine wake tracking and its correlations with wind turbine monitoring sensors. Preliminary results

Cited by 12 publications

References 11 publications

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

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

A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

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