U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

Bodini, Nicola; Lundquist, Julie K.; Kirincich, Anthony R.

doi:10.1029/2019gl082636

Cited by 53 publications

(50 citation statements)

References 50 publications

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“…Martin et al, 2016;Sumner and Masson, 2006;Vanderwende and Lundquist, 2012;Wagner et al, 2010;Walter et al, 2009;Wharton and Lundquist, 2012b). Idealized theories state that the power extracted by a wind turbine is a function of the blade element's efficiency (i.e., turbine blade design) and the available power flux through the disk swept by the blades (Burton et al, 2001). However, atmospheric turbine operating conditions diverge from simplified ones used for turbine design.…”

Section: Introductionmentioning

confidence: 99%

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

Gomez

Lundquist

2020

Wind Energ. Sci.

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Abstract. Numerous studies have shown that atmospheric conditions affect wind turbine performance; however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, and speed shear affect wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Wind direction and speed shear were found to follow a diurnal cycle; however, they evolved differently with increasing wind speeds. Using a combination of speed and direction shear values, we found large direction and small speed shear to result in underperformance. We further analyzed the effects of wind veering on turbine performance for specific values of speed shear and found detrimental conditions on the order of 10 % for wind speed regimes predominantly located in the middle of the power curve. Focusing on a time period of ramping electricity demand (06:00–09:00 LT – local time) exposed the fact that large direction shear occurred during this time and undermined turbine performance by more than 10 %. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance.

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Section: Introductionmentioning

confidence: 99%

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

Gomez

Lundquist

2020

Wind Energ. Sci.

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“…Average values of 0.1 deg m -1 for summertime, and 0.05 deg m -1 for wintertime (Bodini et al, 2019) are near the threshold at which we found significant power reductions. Further, they also found the summer to have low turbulence dissipation rates, thus long-propagating skewed wakes may impact power production and loads on downwind turbines.…”

Section: Discussionmentioning

confidence: 48%

“…Further work regarding directional wind shear in offshore locations should also be pursued. A preliminary wind resource assessment on the coast of Massachusetts by Bodini et al (2019) evidenced large changes in wind direction with height.…”

Section: Discussionmentioning

confidence: 99%

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

Gomez¹,

Lundquist²

2019

Preprint

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Abstract. Numerous studies have shown that atmospheric conditions affect wind turbine performance, however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, affects wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Directional wind shear was found to follow a diurnal cycle and to monotonically decrease with increasing wind speeds. Using different thresholds to distinguish between high- and low-directional wind shear scenarios, we found that larger thresholds evidence statistically-significant effects on turbine power production for lower wind speeds. We further analyzed a threshold of 0.225 deg m−1 and found turbine underperformance in the order of 10 % for wind speed regimes below 8 m s−1. Considering a time period of ramping electricity demand (05:30–09:00 LT) exposed the fact that large direction shear occurs during this time and is undermining turbine performance by more than 10 %. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance, however, future work on segregating speed and direction shear should be pursued to quantify the effects of only one factor on turbine power production.

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“…Bodini et al (2018Bodini et al ( , 2019b showed how has strong diurnal and annual cycles onshore, with topography playing a key role in triggering its variability. On the other hand, offshore turbulence regimes (Bodini et al, 2019a) are characterized by smaller values of , with cycles mostly impacted by wind regimes rather than convective effects. Also, greatly increases in the wakes of obstacles, for example wind turbines (Lundquist and Bariteau, 2015;Wildmann et al, 2019) or whole wind farms (Bodini et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?

Bodini

Lundquist

Optis

2020

Preprint

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Abstract. Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ε) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that a widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ε. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that multivariate polynomial regressions and random forests can eliminate the bias MYNN currently shows in representing ε, while also reducing the average error by up to 30 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ε, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

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U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

Cited by 53 publications

References 50 publications

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?

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