Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1

Fleming, Paul; King, Jennifer R.; Dykes, Katherine; Simley, Eric; Roadman, Jason; Scholbrock, Andrew; Murphy, P. J.; Lundquist, Julie K.; Moriarty, Patrick; Fleming, Katherine; Dam, Jeroen van; Bay, Christopher J.; Mudafort, Rafael; Lopez, Hector Sanchez; Skopek, Jason; Scott, Michael L.; Ryan, Brady; Guernsey, Charles; Brake, Dan

doi:10.5194/wes-2019-5

Cited by 40 publications

(69 citation statements)

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“…Moreover, summertime wakes would be affected by wind veer greater than 0.3 • m −1 ; even larger values occur during wind gusts (Worsnop et al, 2017). This veer can impact effectiveness of wake steering solutions (Fleming et al, 2017(Fleming et al, , 2019 to minimize wake energy loss. Moreover, as wind farm lease areas are more than 25 km offshore, even the stronger turbulence conditions observed in the winter could represent an extreme upper bound on turbulence in the lease areas.…”

Section: Discussionmentioning

confidence: 99%

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

Bodini

Lundquist

Kirincich

2019

Geophysical Research Letters

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The rapid growth of offshore wind energy requires accurate modeling of the wind resource, which can be depleted by wind farm wakes. Turbulence dissipation rate (ϵ) governs the accuracy of model predictions of hub‐height wind speed and the development and erosion of wakes. Here we assess the variability of turbulence kinetic energy and ϵ using 13 months of observations from a profiling lidar deployed on a platform off the Massachusetts coast. Offshore, ϵ is 2 orders of magnitude smaller than onshore, with a subtle diurnal cycle. Wind direction influences the annual cycle of turbulence, with larger values in winter when the wind flows from the land, and smaller values in summer, when the wind flows from open ocean. Because of the weak turbulence, wind plant wakes will be stronger and persist farther downwind in summer.

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Section: Discussionmentioning

confidence: 99%

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

Bodini

Lundquist

Kirincich

2019

Geophysical Research Letters

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“…These simulations evidenced an increase in power production when wind veering was present versus a non-direction-shear condition for certain turbine locations. Fleming et al (2019) proved this power gain by employing a successful approach to taking advantage of wakes in wind farms (wake steering), evidencing approximately 13% increase in energy on a downstream turbine. On their study, Churchfield and Sirnivas (2018) also found an asymmetrical load-behavior variation for turbines displaced laterally compared to an upstream turbine.…”

Section: Discussionmentioning

confidence: 97%

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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“…Importantly, with the development dataset test protocol used in this study, the statistical and physical models can be compared with the same quantity of SCADA data inputs. While the physics-based model has advantages in control oriented approaches (see, e.g., [37,40,44,75]), statistical models reduce the power prediction error. Future work will consider how to incorporate controllability into the pure statistical approaches.…”

Section: Discussionmentioning

confidence: 99%

“…The development of accurate, computationally efficient power production models is particularly useful in the study of wind farms due to their widespread use in layout optimization [32,33] and active-control [34][35][36][37]. Low-order wind farm models continue to be relevant for engineering applications due to the computational expense of fully-resolved [38] or even large-eddy simulations [39].…”

Section: Wind Farm Power Modelsmentioning

confidence: 99%

“…The physics-based, low-order model selected here is the Gaussian wake model [7] with the momentum theory prescribed by Prandtl's lifting line model [8]. The Gaussian wake model is selected due to its success in reproducing the power production for field tests of utility-scale turbines [37] while the lifting line model is chosen since it is suitable for wake steering control optimization [8,44]. The streamwise velocity following a single wind turbine i is modeled as…”

Section: Physics-based Modelmentioning

confidence: 99%

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Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Howland

Dabiri

2019

Energies

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Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

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Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1

Cited by 40 publications

References 1 publication

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

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

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

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

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