Effect of the turbine scale on yaw control

Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano

doi:10.1002/we.2262

Cited by 44 publications

(27 citation statements)

References 31 publications

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“…A similar trend was reported by Krogstad and Adaramola and Bartl et al . Other studies report slightly different exponents for the cosine, such as X =1.8 in Schepers, X =1.88−5.4 in Dahlberg and Montgomerie, while in Ciri et al and Bastankhah and Porté‐Agel, the exponent is between 2< X <3. As expected, there is no difference in the loading between positive and negative yaw misalignment.…”

Section: Blade Loadingsupporting

confidence: 85%

“…The wake deflection obtained from traversing only in the horizontal direction at hub height is close to the experimental deflection obtained by Bartl et al for γ =+30°. This shows that though different Reynolds numbers are used, the wake deflection is similar, unlike results reported in Ciri et al . Increasing the tip‐speed ratio leads to an increase in the horizontal displacement of the wake center.…”

Section: Wake Developmentsupporting

confidence: 69%

“…However, Bartl et al show in their experiments that the spanwise wake deflection is relatively independent of different turbulence levels. Further, Ciri et al report that larger rotor diameters, and the ensuing larger Reynolds number, lead to a larger wake deflection as the wake is then able to retain its coherent structure and initial deflected orientation longer. The impact of different tip‐speed ratios on the wake development was investigated experimentally by Krogstad and Adaramola, who showed that introducing yaw decreases the wake width for all tip‐speed ratios and that the spanwise wake deflection increases with tip‐speed ratio.…”

Section: Introductionmentioning

confidence: 99%

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Parametric dependencies of the yawed wind‐turbine wake development

2020

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Yaw misalignment is currently being treated as one of the most promising methods for optimizing the power of wind farms. Therefore, detailed knowledge of the impact of yaw on the wake development is necessary for a range of operating conditions. This study numerically investigates the wake development behind a single yawed wind turbine operating at different tip-speed ratios and yaw angles using the actuator-line method in the spectral-element code Nek5000. It is shown that depending on the tip-speed ratio, the blade loading varies along the azimuth, resulting in a wake that is asymmetric in both the horizontal and vertical directions. Large tip-speed ratios as well as large yaw angles are shown to decrease the vertical asymmetry of the yaw-induced counter-rotating vortex pair. Both parameters have the effect that they increase the spanwise force induced by yaw relative to the wake rotation. However, while the strength of the counter-rotating vortex pair in the far wake increases with yaw angle, it is shown to decrease with the tip-speed ratio. The vertical shift in the wake center is found to be highly dependent on the yaw angle and the tip-speed ratio. These detailed insights into the yawed wake are important when optimizing potential downstream turbines. KEYWORDS actuator-line method, CFD, spectral-element method, wake breakdown, wind turbine, yaw

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Section: Blade Loadingsupporting

confidence: 85%

Section: Wake Developmentsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

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Parametric dependencies of the yawed wind‐turbine wake development

2020

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“…First, Ciri et al (2018) note that the scale of the vortices have implications for wake steering's effectiveness, which can be determined by the scale of the rotor to atmospheric scales. They state that larger turbines induce larger wake deflections due to the larger rotor's effects on the length and time scales of the vortex structures.…”

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

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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

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“…For example, it is remarkable that wind turbine control optimization can be conceived at the level of each single wind turbine or at the wind farm level. This work deals with the former type of approach; nevertheless it is important to recall that cooperative control [1][2][3] and wake steering [4][5][6][7][8] are two closely related aspects, currently standing at the frontier in the wind energy research: the objective is adopting non-trivial yaw and-or pitch control strategies [9,10], in order to optimize the power production and possibly mitigate mechanical loads at the level of wind farm.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Yaw Control Optimization and Its Impact on Performance

2019

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The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.

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Effect of the turbine scale on yaw control

Cited by 44 publications

References 31 publications

Parametric dependencies of the yawed wind‐turbine wake development

Parametric dependencies of the yawed wind‐turbine wake development

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

Wind Turbine Yaw Control Optimization and Its Impact on Performance

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