Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1

Fleming, Paul; King, Jennifer; Dykes, Katherine; Simley, Eric; Roadman, Jason; Scholbrock, Andrew; Murphy, Patrick C.; Lundquist, Julie K.; Moriarty, Patrick; Fleming, Katherine; Dam, Jeroen van; Bay, Christopher J.; Mudafort, Rafael; Lopez, H.M. Villanueva; Skopek, Jason; Scott, Mike; Ryan, Brady; Guernsey, Charles; Brake, Dan

doi:10.5194/wes-4-273-2019

Cited by 194 publications

(181 citation statements)

References 23 publications

Supporting

Mentioning

178

Contrasting

Order By: Relevance

“…Due to the proprietary nature of the information, wind turbine controller design was not provided by the turbine manufacturer. Not having access to wind turbine controller design is a major challenge to fully understanding wind turbines (Fleming et al, 2019), or rather, how the controller responds to variable inflow conditions when attempting to enact the desired control offsets. Therefore, the provided discussions do not detail why the turbine was able or unable to enact the desired control changes but rather focuses on quantifying the resulting offsets.…”

Section: T T Controller Assessmentmentioning

confidence: 99%

“…11) (Drobinski and Foster, 2003;Träumner et al, 2015). Although these features are elongated in the along-wind dimension, their orientation can slightly deviate (i.e., clockwise or counterclockwise) from the ABL wind direction (Morrison et al, 2005;Foster, 2005;Lorsolo et al, 2008). Although it is not fully understood what causes streak orientation to differ from the ABL wind direction, it can be hypothesized that the same boundary layer forcings also promote downstream wake deviation from the ABL wind direction.…”

Section: Abl Streak Orientationmentioning

confidence: 99%

“…Experimental validation of wind plant control at full scale has frequently relied upon the analysis of power and controls data from individual turbine pairs in a wind plant to quantify the benefit of various wind plant control techniques (e.g., Fleming et al, 2017aFleming et al, , 2019Ahmad et al, 2019;Van der Hoek et al, 2019;Howland et al, 2019). However, few studies have used advanced measurement technologies (such as lidar or radar) to document differences in wake structure due to the turbine control changes implemented as part of wind plant control (e.g., Trujillo et al, 2016;Fleming et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Hirth

Schroeder

2020

Wind Energ. Sci.

View full text Add to dashboard Cite

Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utilityscale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g., boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicates the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.Published by Copernicus Publications on behalf of the European Academy of Wind Energy e.V.

show abstract

Section: T T Controller Assessmentmentioning

confidence: 99%

Section: Abl Streak Orientationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Hirth

Schroeder

2020

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…The goal of wake steering is to deflect the wake away from the downwind turbine by using the yaw settings of the upwind turbine. Field experiments showing promising results were conducted by Fleming et al (2017Fleming et al ( , 2019; Howland et al (2019). In these experiments lookup tables with optimal yaw settings of each turbine are created with help of an steady-state model.…”

Section: Introductionmentioning

confidence: 99%

Real-time optimization of wind farms using modifier adaptation and machine learning

Andersson¹,

Imsland²

2020

Preprint

View full text Add to dashboard Cite

Abstract. Real-time optimization (RTO) covers a family of optimization methods that incorporate process measurements in the optimization to drive the real process (plant) to optimal performance while guaranteeing constraint satisfaction. Modifier Adaptation (MA) introduces zeroth and first-order correction terms (bias and gradients) for the cost and constraint functions. Instead of updating the plant model, in MA the optimization problem is updated directly from data guaranteeing to meet the necessary condition of optimality upon convergence. The main burden of the MA approach is the estimation of the first-order modifiers of the cost and constraint functions at each RTO iteration. Finite-difference approximation is the most common approach that requires at least nu + 1 steady-state operation points to estimate the gradients, where nu is the number of control inputs. Obtaining these can require a long convergence time. For this reason, this work considers the use of Gaussian process (GP) regression to estimate the plant-model mismatch based on plant measurements, and replace the usual modifiers by these high order regression functions. GP is a probabilistic, non-parametric modelling technique well known in the machine learning community. The approach is tested on several numerical test cases simulating wind farms. It is shown that the approach is able to correct the model and converges to the plant optimal point. Several improvements for large inputs spaces, which is a challenging problem for the approach presented in the article, are discussed.

show abstract

“…Despite these encouraging results, full-scale validation of wind plant control and measurement of wind turbine wake response to turbine control changes remains limited (e.g. Marathe et al, 2016;Trujillo et al, 2016;Fleming et al, 2017a;Fleming et al, 2017b;Ahmad et al, 2019;van der Hoek et al, 2019;Fleming et al, 2019).…”

mentioning

confidence: 99%

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Hirth¹,

Schroeder²

2019

Preprint

View full text Add to dashboard Cite

Abstract. Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utility-scale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analysing wind plant control at full-scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g. boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicate the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.

show abstract

Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1

Cited by 194 publications

References 23 publications

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Real-time optimization of wind farms using modifier adaptation and machine learning

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Contact Info

Product

Resources

About