Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Duc, Thomas; Coupiac, Olivier; Girard, Nicolas; Giebel, Gregor; Göçmen, Tuhfe

doi:10.5194/wes-4-287-2019

Cited by 37 publications

(26 citation statements)

References 28 publications

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“…The wind plant used for the experiment is Sole du Moulin Vieux (SMV), a commercial wind plant operated by EN-GIE Green. It is located in the northern part of France, approximately midway between Paris and Lille, and was already used in previous field tests as part of the SMARTE-OLE project (Ahmad et al, 2017;Duc et al, 2019). It consists of seven Senvion MM82 wind turbines (rotor diameter of D = 82 m, nominal power of 2050 kW, hub height of 80 m) organized in a north-south axis, as shown by the layout in Fig.…”

Section: Field Experiments Overviewmentioning

confidence: 99%

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Simley

Fleming

Girard³

et al. 2021

Wind Energ. Sci.

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Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

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Section: Field Experiments Overviewmentioning

confidence: 99%

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Simley

Fleming

Girard³

et al. 2021

Wind Energ. Sci.

Self Cite

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“…SCADA data were provided for each turbine as 10 min averages and standard deviation of wind speed, power output, rotor rotational velocity, and yaw angle. For more details on this dataset and used quality control process see El-Asha et al (2017) and Zhan et al (2020a).…”

Section: Lidar Experiments For a Wind Farm On Flat Terrainmentioning

confidence: 99%

“…Wake interactions are responsible for significant power losses of wind farms (Barthelmie, et al, 2007;El-Asha et al, 2017), and thus numerical tools for predicting the intra-windfarm velocity field are highly sought after for the optimal design of wind farm layout (Kusiak and Song, 2010;González et al, 2010;Santhanagopalan et al, 2018b), development of control algorithms for improving turbine operations (Lee et al, 2013;Annoni et al, 2016), and enhancement of accuracy in predictions of power capture (Tian et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of engineering wake models through lidar measurements

2020

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Abstract. Engineering wake models provide the invaluable advantage to predict wind turbine wakes, power capture, and, in turn, annual energy production for an entire wind farm with very low computational costs compared to higher-fidelity numerical tools. However, wake and power predictions obtained with engineering wake models can be insufficiently accurate for wind farm optimization problems due to the ad hoc tuning of the model parameters, which are typically strongly dependent on the characteristics of the site and power plant under investigation. In this paper, lidar measurements collected for individual turbine wakes evolving over a flat terrain are leveraged to perform optimal tuning of the parameters of four widely used engineering wake models. The average wake velocity fields, used as a reference for the optimization problem, are obtained through a cluster analysis of lidar measurements performed under a broad range of turbine operative conditions, namely rotor thrust coefficients, and incoming wind characteristics, namely turbulence intensity at hub height. The sensitivity analysis of the optimally tuned model parameters and the respective physical interpretation are presented. The performance of the optimally tuned engineering wake models is discussed, while the results suggest that the optimally tuned Bastankhah and Ainslie wake models provide very good predictions of wind turbine wakes. Specifically, the Bastankhah wake model should be tuned only for the far-wake region, namely where the wake velocity field can be well approximated with a Gaussian profile in the radial direction. In contrast, the Ainslie model provides the advantage of using as input an arbitrary near-wake velocity profile, which can be obtained through other wake models, higher-fidelity tools, or experimental data. The good prediction capabilities of the Ainslie model indicate that the mixing-length model is a simple yet efficient turbulence closure to capture effects of incoming wind and wake-generated turbulence on the wake downstream evolution and predictions of turbine power yield.

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“…Hence, the value

k^{*} =

0.04 is typically used nowadays. Moreover, very recently, k ∗ has been expressed as function of the turbulence intensity, which could further improve the model's predictions 14,44,45 . In the current study, we assume

k^{*} =

0.04.…”

Section: Cases Description and Single‐wake Model Setupmentioning

confidence: 99%

A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields

Lanzilao

Meyers

2021

Wind Energy

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The difference in surface roughness between land and sea, and the terrain complexities, lead to spatially heterogeneous atmospheric conditions, and therefore affect the propagation and dynamics of wind‐turbine and wind‐farm wakes. Currently, these flow heterogeneities and their effects on plant aerodynamics are not modeled in the majority of engineering wake models. In this study, we address this issue by developing a new wake‐merging method capable of superimposing the waked flow on a heterogeneous background velocity field. We couple the proposed wake‐merging method with four different wake models, i.e. the Gaussian, super‐Gaussian, double‐Gaussian and Ishihara model, and we test its performance against LES results, dual‐Doppler radar measurements and SCADA data from the Horns Rev, London Array, and Westermost Rough farms. The standard Jensen model with quadratic superposition is also included. In homogeneous conditions, the new method predicts slightly higher velocity deficits than the linear superposition method. Overall, the distributions of the difference in power ratio between the two wake‐merging methods predictions and observations show a similar mean absolute error (MAE) and interquartile range (IQR) in such conditions. On the other hand, the new wake‐merging method predictions display a lower MAE with a similar IQR in case of a spatially varying background velocity, being overall more accurate than the ones obtained with linear superposition. The most accurate estimates are obtained when the wake‐merging methods are coupled with the double‐Gaussian and Gaussian single‐wake models. In contrast, the Jensen and super‐Gaussian wake models overestimate the velocity deficits for the majority of cases analyzed.

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Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Cited by 37 publications

References 28 publications

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Optimal tuning of engineering wake models through lidar measurements

A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields

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