Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

Lin, Mou; Porté‐Agel, Fernando

doi:10.3390/en12234574

Cited by 40 publications

(40 citation statements)

References 40 publications

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“…The analytical model of Jiménez et al (2009) overestimates the wake deflection, and the models by Bastankhah and Porté-Agel (2016) and Qian and Ishihara (2018) better match the wake deflection from the field measurements. The overestimation of the Jiménez et al (2009) model was also observed by Bastankhah and Porté-Agel (2016) with wind tunnel experiments and by Lin and Porté-Agel (2019) with numerical simulations. The measurement data show considerably larger scattering than the model predictions, which is likely a consequence of the remaining non-stationarity of the atmospheric boundary layer in the data set and the measurement errors of the yaw angle.…”

Section: Wake Deflectionsupporting

confidence: 65%

“…Both 2D and 3D scans of wake-scanning lidar for control cases with a successfully detected wake centre are used. merical simulations (Lin and Porté-Agel, 2019). The analytical model of Jiménez et al (2009) overestimates the wake deflection, and the models by Bastankhah and Porté-Agel (2016) and Qian and Ishihara (2018) better match the wake deflection from the field measurements.…”

Section: Wake Deflectionmentioning

confidence: 75%

“…1). The lidar uses a laser wavelength of 1.54 µm and internally computes the wind speed (U WC ) and wind direction (dir WC ) from a plan position indicator (PPI) scan with an azimuth step of 90 • and 1256 P. Brugger et al: Lidar measurements of yawed-wind-turbine wakes an elevation angle of 62 • followed by a vertical beam with the Doppler beam-swinging technique, assuming horizontal homogeneity (similar to the lidar in Lundquist et al, 2017). The measurement data were filtered with a signal-to-noise ratio (SNR) threshold of −22 dB.…”

Section: Windcubementioning

confidence: 99%

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Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

et al. 2020

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Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

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Section: Wake Deflectionsupporting

confidence: 65%

Section: Wake Deflectionmentioning

confidence: 75%

Section: Windcubementioning

confidence: 99%

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Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

et al. 2020

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“…The wind turbine forcing is represented by f i and an actuator disk model is used (Calaf et al, 2010). While the actuator disk model is lower fidelity than the actuator line methods, it captures the far wake accurately for both aligned (Martínez-Tossas et al, 2015) and yaw misalignment wind turbines (Lin and Porté-Agel, 2019). Since the goal of the present study is controller synthesis and sensitivity experiments, more computationally expensive actuator line simulations are left for future work given the large volume of simulations that are run.…”

Section: Large Eddy Simulation Setupmentioning

confidence: 99%

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

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Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

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“…Linear superposition of the individual wakes is assumed in Eq. (1) (Lissaman, 1979). The wake centerline y c, i is given by…”

Section: Lifting Line Wake Modelmentioning

confidence: 99%

Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions

et al. 2020

View full text Add to dashboard Cite

Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions in which the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven ensemble Kalman filter (EnKF) state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six-turbine array embedded in a statistically quasi-stationary, conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller statistically significantly increases power production compared to the baseline, greedy, yaw-aligned control provided that the EnKF estimation is constrained and informed with a physics-based prior belief of the wake model parameters. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that underperform the baseline yaw-aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over the greedy operation, while underestimating the power reduction leads to decreased power; therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. The EnKF-augmented wake model predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02P1, with P1 as the power of the leading turbine at the farm. A standard wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11P1, demonstrating that state estimation improves the predictive capabilities of simplified wake models.

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Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

Cited by 40 publications

References 40 publications

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions

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