Wake steering via yaw control in multi-turbine wind farms: Recommendations based on large-eddy simulation

Archer, Cristina L.; Vasel-Be-Hagh, Ahmadreza

doi:10.1016/j.seta.2019.03.002

Cited by 68 publications

(43 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wake of a yaw misaligned turbine is narrower than the same turbine when yaw aligned (Archer and Vasel-Be-Hagh, 2019).…”

Section: Dynamic Wake Steering Uniform Inflow Lesmentioning

confidence: 99%

“…However, recent attention has focused on wake steering, the intentional misalignment of certain turbines within a wind farm in order to deflect wakes laterally away from downwind generators (Grant et al, 1997;Jiménez et al, 2010). While the yaw misaligned wind turbine's power production is decreased (Burton et al, 2011;Medici, 2005), wake steering has been shown to increase the power production of downwind generators in simulations (Fleming et al, 2016;Gebraad et al, 2017;Fleming et al, 2018;Archer and Vasel-Be-Hagh, 2019) and wind tunnel experiments (Adaramola and Krogstad, 2011;Mühle et al, 2018;Bastankhah and Porté-Agel, 2019). Further, the potential for wake steering to increase wind farm power production in wind conditions with wake losses has been observed in full-scale field campaigns with two (Fleming et al, 2017(Fleming et al, , 2019 and six wind turbines .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

show abstract

“…The wake of a yaw misaligned turbine is narrower than the same turbine when yaw aligned (Archer and Vasel-Be-Hagh, 2019).…”

Section: Dynamic Wake Steering Uniform Inflow Lesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition to optimizing turbine locations, yaw optimization and control of individual turbines can also reduce wake loss and improve the power production [22][23][24][25][26][27][28][29][30][31][32]. By yawing upstream turbines, wakes can be deflected away from downstream turbines.…”

Section: Introductionmentioning

confidence: 99%

“…By yawing upstream turbines, wakes can be deflected away from downstream turbines. In particular, several studies [28,29,31,32] were conducted to study the potential improvement of controlling turbine yaw angles. Fleming et al [24] performed an numerical analysis on two turbines and concluded that the overall power production could be increased with optimized yaw angles.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Yaw Optimization via Random Search Algorithm

Kuo

Pan

et al. 2020

Energies

View full text Add to dashboard Cite

One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

show abstract

“…Since reducing the vortices in the wake area of a wind turbine can improve not only the power generation efficiency but also the reliability of the turbines in the wake area, active wake management (AWM) technology has been identified as a promising way to increase the power production, reduce operation and maintenance cost, and therefore cut down the levelized cost of a wind project [4]. The core thought of the AWM technology is to reduce the wake losses of the downstream wind turbines by performing the yaw operation of upstream turbines [5]. Such technology increases the added value of yaw control, which was originally designed to adapt the turbine to the changes in wind speed and wind direction [6].…”

Section: Introductionmentioning

confidence: 99%

Further Study on the Effects of Wind Turbine Yaw Operation for Aiding Active Wake Management

Dai

Yang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Active wake management (AWM) via yaw control has been discussed in recent years as a potential way to improve the power production of a wind farm. In such a technique, the wind turbines will be required to work frequently at misaligned yaw angles in order to reduce the vortices in the wake area behind the turbines. However, today, it is still not very clear about how yaw operation affects the dynamics and power generation performance of the wind turbines. To further understand the effects of yaw operation, numerical research is conducted in this paper. In the study, the optimal size of the flow field used in the computational fluid dynamics (CFD) calculation was specifically discussed in order to obtain an efficient numerical model to quickly and accurately predict the dynamics and the performance of the turbines. Through this research, the correlation between the blade loads during yaw and non-yaw operations is established for aiding yaw control, and the blade loads and power generation performances of the wind turbine during yaw operation under different wind shear and blade deflection conditions are analyzed for understanding the effects of yaw operation. It is found that the optimal size of the flow field for performing efficient and accurate CFD calculations does exist. The misaligned yaw operation generally tends to decrease the loads acting on the blade. However, the aerodynamic energy captured by the turbine rotor and blade loads during yaw operation is not only dependent on the yaw angle of the rotor but is also affected by wind speed, rotor speed, the pitch angle of the blades, blade deflection, and wind shear. Particularly, it is interestingly found that wind shear can cause undesirable fluctuation of the power, which will challenge the power quality of the wind farm if no measures are taken.

show abstract

Wake steering via yaw control in multi-turbine wind farms: Recommendations based on large-eddy simulation

Cited by 68 publications

References 41 publications

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Wind Farm Yaw Optimization via Random Search Algorithm

Further Study on the Effects of Wind Turbine Yaw Operation for Aiding Active Wake Management

Contact Info

Product

Resources

About