Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines

Bartl, Jan; Mühle, Franz; Sætran, Lars Roar

doi:10.5194/wes-3-489-2018

Cited by 54 publications

(46 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, it might rather be the preferred control setting of the downstream turbine even with a greedy controller, as the turbine aims to align itself with the flow automatically. Turbines reorienting themselves to improve power performance while operating in wake have also been observed in full-scale measurements by McKay et al (2013) and experimentally observed by Bartl et al (2018b).…”

Section: Wake Deflection and Velocity Deficitmentioning

confidence: 57%

“…This was combined with a multi-zone wake deficit model in FLORIS (FLOw Redirection and Induction in Steady State). The FLORIS framework, see Annoni et al (2018), now includes an alternative wake model by Bastankhah and Porté-Agel (2014), which is further extended in Bastankhah and Porté-Agel (2016). However, FLORIS yields time averaged results for power prediction, while a model that combines the estimation of both power output and the structural loading on the wind turbine is crucial in order to apply yaw-based wind farm control.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

View full text Add to dashboard Cite

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

show abstract

Section: Wake Deflection and Velocity Deficitmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

View full text Add to dashboard Cite

show abstract

“…Wake deflection due to yaw misalignment has also been studied in wind tunnel experiments (Medici and Alfredsson, 2006;Schottler et al, 2017;Bartl et al, 2018). Park et al (2016) conducted an optimal yaw angle study for six scaled turbines in a wind tunnel using a data-driven Bayesian Ascent method.…”

mentioning

confidence: 99%

Unlocking the Full Potential of Wake Steering: Implementation and Assessment of a Controls-Oriented Model

Bay

King

Fleming

et al. 2019

Preprint

View full text Add to dashboard Cite

In this work, a controls-oriented wake model is modified and compared to an analytical Gaussian wake model and high-fidelity simulation data. This model, called the curled wake model, captures a wake phenomenon that occurs behind yawed turbines, modeled as a collection of vortices shed from the rotor plane. Through turbine simulations, these vortices are shown to have a significant impact on the prediction of wake steering's performance. Also, optimizations using the model are performed and produce results consistent with recent published research. Results indicate that wind farm controllers designed 5 and analyzed with the curled wake model produce wake steering controllers which can realize larger gains in power production than previously estimated. Overall, the results support the concept of secondary steering, or a yawed turbine's ability to deflect the wake of a downstream turbine, and suggest that future turbine wake studies and yaw optimizations should include the curled wake phenomenon.have been conducted to evaluate the effectiveness of wake steering (Wagenaar et al., 2012;Fleming et al., 2017). Fleming et al. (2019) found a 13% increase in energy on a downstream turbine for two closely spaced turbines within a 10 • bin, consistent with prior predictions from different models. Howland et al. (2019) leveraged site-specific, historical operational data to develop a wake control scheme that was tested in a land-based wind power plant in Alberta, Canada. The controller resulted in power increases of 7%-47% for wind conditions that commonly occur at night, as well as a decrease in variability 5 of power production of up to 72%.Alongside theoretical and experimental studies, engineering models have been crafted to predict wake steering and assess its overall benefit. One controls-oriented tool used in the design and study of turbine wake controls is the FLOw Redirection and Induction in Steady State (FLORIS) model . Originally based on the Jensen (Jensen, 1984) andJiménez (Jiménez et al., 2010) models, FLORIS has evolved into a software repository that contains several wake models. More 10

show abstract

“…7b show a slightly curled wake shape. The curled wake shape was shown to develop from a counter-rotating vortex pair, as discussed in detail by Schottler et al (2018a) and Bartl et al (2018b) for the same experimental data set. Similar flow physics behind a yawed turbine were observed in simulations by a full-scale turbine by Howland et al (2016) and Vollmer et al (2016).…”

Section: Statistical Methods For Wake Propertiesmentioning

confidence: 55%

Blind test comparison on the wake behind a yawed wind turbine

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract. This article summarizes the results of the “Blind test 5” workshop, which was held in Visby, Sweden, in May 2017. This study compares the numerical predictions of the wake flow behind a model wind turbine operated in yaw to experimental wind tunnel results. Prior to the workshop, research groups were invited to predict the turbine performance and wake flow properties using computational fluid dynamics (CFD) methods. For this purpose, the power, thrust, and yaw moments for a 30∘ yawed model turbine, as well as the wake's mean and turbulent streamwise and vertical flow components, were measured in the wind tunnel at the Norwegian University of Science and Technology (NTNU). In order to increase the complexity, a non-yawed downstream turbine was added in a second test case, while a third test case challenged the modelers with a new rotor and turbine geometry. Four participants submitted predictions using different flow solvers, three of which were based on large eddy simulations (LES) while another one used an improved delayed detached eddy simulation (IDDES) model. The performance of a single yawed turbine was fairly well predicted by all simulations, both in the first and third test cases. The scatter in the downstream turbine performance predictions in the second test case, however, was found to be significantly larger. The complex asymmetric shape of the mean streamwise and vertical velocities was generally well predicted by all the simulations for all test cases. The largest improvement with respect to previous blind tests is the good prediction of the levels of TKE in the wake, even for the complex case of yaw misalignment. These very promising results confirm the mature development stage of LES/DES simulations for wind turbine wake modeling, while competitive advantages might be obtained by faster computational methods.

show abstract

Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines

Cited by 54 publications

References 34 publications

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Unlocking the Full Potential of Wake Steering: Implementation and Assessment of a Controls-Oriented Model

Blind test comparison on the wake behind a yawed wind turbine

Contact Info

Product

Resources

About