Detection of wakes in the inflow of turbines using nacelle lidars

Held, Dominique Philipp; Mann, Jakob

doi:10.5194/wes-4-407-2019

Cited by 21 publications

(17 citation statements)

References 31 publications

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“…From the results presented in part 3.1, it is shown that the wind regimes in neutral and unstable boundary layers have large difference even under the same low surface roughness length z 0 = 0.001 m. Under the effect of surface temperature flux, the convective boundary layer forms a wind regime with less shear and higher turbulence intensity compared with that in neutral condition, leading to the larger Reynolds stress and thus faster velocity recovery in the wind turbine wake flow. The real-time preview control strategy needs the accurate capture of the dynamic wake characteristics to reconstruct to a certain extent the inhomogeneous inflow field of every wind turbine [66,67]. The wake flow structure corresponding to the meandering or larger scale is the most important information and feasible to model [65].…”

Section: Discussionmentioning

confidence: 99%

LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Wan

2019

Sustainability

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Wake meandering disturbs the stability of the far wake field and thus increases the fatigue loads of downstream wind turbines. A deep understanding of this phenomenon under atmospheric boundary layers and its relation to the structural loads helps to better model the dynamic wake and alleviate adverse effects. A large eddy simulation and an actuator line model are introduced in the present work to simulate the wake field and aerodynamic loads of wind turbines with different longitudinal spacings. By temporal filtering and the gaussian fitting method, the wake center and edge are precisely defined, and the dynamic wake characteristics, including the wake width, oscillation amplitude, and frequency, are described based on the statistical data of the simulated flow field. Results reveal that the wake meandering is caused by both large-scale atmospheric structure and the unstable vortex shed from the rotor because two distinct meandering frequency ranges are detected. As the atmosphere instability increases, the former becomes the dominant inducing factor of the meandering movements. Further, the analysis of the correlation between the inflow characteristics and the wake deflection shows that the Taylor hypothesis remains valid within a distance of over a thousand meters under both neutral and convective boundary layers, proving the feasibility of using this hypothesis for wake evolution prediction. In addition, our study shows that the fluctuation of blade root moment and yaw moment is significantly intensified by the meandering wake, with their standard deviation is augmenting by over two times under both atmospheric conditions. The power spectrum illustrates that the component with rotor rotation frequency of the former is sensible to the wake effect, but for the latter, the power spectrum density of all frequencies is increased under the meandering wake. These indicate that the fatigue loads will be underestimated without considering the wake meandering effect. Moreover, the high correlation between the wake deflection and yaw moment implies that we can predict yaw moment based on the incoming flow information with high accuracy.

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Section: Discussionmentioning

confidence: 99%

LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Wan

2019

Sustainability

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“…blades, shaft, tower) are represented by a number of bodies, which are defined as an assembly of Timoshenko beam elements (Larsen et al, 2013). The aerodynamic part of the code is based on the blade element momentum (BEM) theory, extended to handle dynamic inflow and dynamic stall (Hansen et al, 2004), among others. Top and front views of the CW lidar (a, b) and PL lidar (c, d) scanning patterns shown by the blue dots.…”

Section: Methodsmentioning

confidence: 99%

Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements

2020

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Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4 %–11 %). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.

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“…The algorithm relies on 10-min statistics of the lidar measurements and follows the approach of Held and Mann (2019). The idea is to detect the increase in turbulence originating from wakes with respect to the free wind conditions.…”

Section: Wake Detection Algorithmmentioning

confidence: 99%

“…We select a log-normal and normal pdf for T I and measurements at several ranges are omitted in the present work. Improved wake detection can be obtained by establishing thresholds conditional to the ambient wind conditions (i.e., wind speed, turbulence and atmospheric stability) and by assessing the detection parameters for shorter time periods (Held and Mann, 2019). More detailed detection algorithms including wake dynamic characteristics are proposed in the literature .…”

Section: Wake Detection Algorithmmentioning

confidence: 99%

“…In addition, we observe a steeper slope of the wake spectra towards higher wavenumbers, which reduces the turbulence energy content within the range of rotor sampling frequencies. Small-scale turbulence is also responsible for increasing the width of the Doppler spectrum (Branlard et al, 2013;Held and Mann, 2019). We show an example of a 10-min ensemble-average Doppler spectrum obtained from the radial velocity of the CW lidar using bins b3 and b8 (see Fig.…”

Section: Wake Effects On Reconstructed Wind Parametersmentioning

confidence: 99%

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Aero-elastic load validation in wake conditions using nacelle-mounted lidar measurements

Conti¹,

Dimitrov²,

Peña³

2020

Preprint

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Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed and a continuous wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial- and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters and wake disturbances are present for two opposite wind direction sectors. We account for wake-induced effects by means of wind field parameters commonly used as inputs for load simulations, which are reconstructed using lidar measurements. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error and turbulence-spectra parameters. The uncertainty and bias of aero-elastic load predictions are quantified against wind turbine on-board sensor data. We consider mast-based load assessments in free wind as a reference case and assess the uncertainty in lidar-based power and load predictions when the turbine is operating in partial- and full-wake. Compared to the reference case, the simulations in wake conditions lead to an increase of the relative error as low as 4 %. It is demonstrated that the mean wind speed, turbulence intensity and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a rotor equivalent wind speed model are the most challenging aspects of load validation in wake conditions.

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Detection of wakes in the inflow of turbines using nacelle lidars

Cited by 21 publications

References 31 publications

LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements

Aero-elastic load validation in wake conditions using nacelle-mounted lidar measurements

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