A Large Eddy Simulation framework to assess wind farm power maximization strategies: Validation of maximization by yawing

Draper, Martín; Guggeri, Andrés; López, Bruno; Diaz, Alfredo Peña; Campagnolo, Filippo; Usera, Gabriel

doi:10.1088/1742-6596/1037/7/072051

Cited by 11 publications

(13 citation statements)

References 23 publications

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“…LES studies have shown that an incorrect estimate for P p can lead to suboptimal wake steering performance 25 . Draper et al (2018) 20 found that the P p for a waked turbine depends on the yaw misalignment of the upwind turbine and fit experimental coefficient of power C p curves to find that 1.3 < P p < 2.5. Liew et al (2020) 11 recently demonstrated in LES that P p = 3 is a poor estimate for wind turbines in yaw misalignment with complex, non-uniform incident wake flow and found that the value of P p depends on the incident wind conditions.…”

Section: Introductionmentioning

confidence: 92%

“…Bartl et al (2018) 5,18 found that P p ≈ 3 for a rotating wind turbine model in wind tunnel experiments with low and high turbulence uniform inflow and sheared inflow conditions. Schreiber et al (2017) 19 and Draper et al (2018) 20 used wind tunnel experiments and LES to show that P p ≈ 1.8 for a wind turbine in sheared freestream conditions. Fleming et al (2017) 6 found P p ≈ 1.4 for the Envision 4 MW turbine using LES and confirmed this value in a field experiment, although the number of data points beyond |γ| > 10 • were limited.…”

Section: Introductionmentioning

confidence: 99%

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Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

Howland

Gonzalez

Martínez

et al. 2020

Journal of Renewable and Sustainable Energy

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The intentional yaw misalignment of leading, upwind turbines in a wind farm, termed wake steering, has demonstrated potential as a collective control approach for wind farm power maximization. The optimal control strategy and the resulting effect of wake steering on wind farm power production are in part dictated by the power degradation of the upwind yaw misaligned wind turbines. In the atmospheric boundary layer, the wind speed and direction may vary significantly over the wind turbine rotor area, depending on atmospheric conditions and stability, resulting in freestream turbine power production which is asymmetric as a function of the direction of yaw misalignment and which varies during the diurnal cycle. In this study, we propose a model for the power production of a wind turbine in yaw misalignment based on aerodynamic blade elements, which incorporates the effects of wind speed and direction changes over the turbine rotor area in yaw misalignment. The proposed model can be used for the modeling of the angular velocity, aerodynamic torque, and power production of an arbitrary yaw misaligned wind turbine based on the incident velocity profile, wind turbine aerodynamic properties, and turbine control system. A field experiment is performed using multiple utility-scale wind turbines to characterize the power production of yawed freestream operating turbines depending on the wind conditions, and the model is validated using the experimental data. The resulting power production of a yaw misaligned variable speed wind turbine depends on a nonlinear interaction between the yaw misalignment, the atmospheric conditions, and the wind turbine control system.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

Howland

Gonzalez

Martínez

et al. 2020

Journal of Renewable and Sustainable Energy

View full text Add to dashboard Cite

show abstract

“…While many studies have performed proofs of concepts using only two or three turbines, 26,33,40,47,102,106,109,111,112,119,123,125,131,132,134,138,143,151,161–164 wake steering is more complicated in a wind farm. Fortunately, and in contrast to AIC, many of the lessons learned with a column of turbines still apply in larger arrays.…”

Section: Wake Management Techniquesmentioning

confidence: 99%

“…Fortunately, and in contrast to AIC, many of the lessons learned with a column of turbines still apply in larger arrays. Both optimization techniques 3,36,102,103,111,114,119,120,128,137,140,145,148–150,157,158,161,163–165 and trial and error comparisons 108,123,164 of different arrangements of yaw misalignment angles have been tested and have arrived at different conclusions. Several studies have found that total power is optimized when the upstream turbines are misaligned the most and the misalignment angle is decreased as turbines are located farther downstream 3,108,123,146,164 .…”

Section: Wake Management Techniquesmentioning

confidence: 99%

“…Both optimization techniques 3,36,102,103,111,114,119,120,128,137,140,145,148–150,157,158,161,163–165 and trial and error comparisons 108,123,164 of different arrangements of yaw misalignment angles have been tested and have arrived at different conclusions. Several studies have found that total power is optimized when the upstream turbines are misaligned the most and the misalignment angle is decreased as turbines are located farther downstream 3,108,123,146,164 . Gebraad et al 136 used a game theoretic approach and found that a 3 × 2 wind farm was optimized when the front row had misalignment angles of about 25°, the second row about 40°, and the back row was not misaligned as usual.…”

Section: Wake Management Techniquesmentioning

confidence: 99%

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Review of wake management techniques for wind turbines

Houck

2021

Wind Energy

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Summary The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.

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Actuator Line Model simulations to study active power control at wind turbine level

Guggeri

Draper

López

et al. 2019

J. Phys.: Conf. Ser.

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Wind energy is expanding rapidly worldwide, being horizontal axis wind turbines the technology of larger installed capacity. Modern multi-MW wind turbines have a torque controller and a collective pitch controller to control their power output, particularly when the wind speed is greater than the rated one, or when it is required to down-regulate the turbines’ production. In this work we show results of a validated numerical method [1], based on a Large Eddy Simulation-Actuator Line Model framework, applied to evaluate active power control on a real 7.7MW [2][3] onshore wind farm of Uruguay. We describe the implementation of these controllers in the caffa3d solver [4] and present the methodology we applied to obtain the controller parameters, such as the gain scheduling of the closed loop Proportional-Integral pitch controller. For validation, the simulation results are compared with 1Hz data obtained from the Supervisory Control and Data Acquisition System of the wind farm, focusing on the temporal evolution of the following variables: wind velocity, rotor angular speed, pitch, aerodynamic and electric torque and power. We analyze the Active Power Control response under different de-rate signals, both constant and time-varying, and subject to two wind profiles and two different wind directions, one of them with significant influence of wakes on one wind turbine. The dependence of the wake on the de-rate value is also evaluated, assessing the streamwise velocity component and the turbulence intensity in the wake.

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A Large Eddy Simulation framework to assess wind farm power maximization strategies: Validation of maximization by yawing

Cited by 11 publications

References 23 publications

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

Review of wake management techniques for wind turbines

Actuator Line Model simulations to study active power control at wind turbine level

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