Survey of wind farm control—power and fatigue optimization

Knudsen, Torben; Bak, Thomas; Svenstrup, Mikael

doi:10.1002/we.1760

Cited by 203 publications

(156 citation statements)

References 43 publications

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“…This can be achieved by redirecting propagating wakes (yaw control; see, e.g., Fleming et al, 2014;Gebraad et al, 2016;Campagnolo et al, 2016) or by affecting the induced wake velocity deficits (axial induction control; see, e.g., Nilsson et al, 2015;Annoni et al, 2016;Bartl and Saetran, 2016). A more exhaustive survey of wind-farm control in a broader context can be found in Knudsen et al (2015) and Boersma et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

Munters

Meyers

2018

Wind Energ. Sci.

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Abstract. Wake interactions between wind turbines in wind farms lead to reduced energy extraction in downstream rows. In recent work, optimization and large-eddy simulation were combined with the optimal dynamic induction control of wind farms to study the mitigation of these effects, showing potential power gains of up to 20 % (Munters and Meyers, 2017, Phil. Trans. R. Soc. A, 375, 20160100, https://doi.org/10.1098/rsta.2016.010). However, the computational cost associated with these optimal control simulations impedes the practical implementation of this approach. Furthermore, the resulting control signals optimally react to the specific instantaneous turbulent flow realizations in the simulations so that they cannot be simply used in general. The current work focuses on the detailed analysis of the optimization results of Munters and Meyers, with the aim to identify simplified control strategies that mimic the optimal control results and can be used in practice. The analysis shows that wind-farm controls are optimized in a parabolic manner with little upstream propagation of information. Moreover, turbines can be classified into first-row, intermediate-row, and last-row turbines based on their optimal control dynamics. At the moment, the control mechanisms for intermediate-row turbines remain unclear, but for first-row turbines we find that the optimal controls increase wake mixing through the periodic shedding of vortex rings. This behavior can be mimicked with a simple sinusoidal thrust control strategy for first-row turbines, resulting in robust power gains for turbines in the entrance region of the farm.

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Section: Introductionmentioning

confidence: 99%

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

Munters

Meyers

2018

Wind Energ. Sci.

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“…In order to mitigate power losses and wake-induced loads on downstream turbines, different upstream turbine control strategies have recently been suggested (Knudsen et al, 2014;Gebraad et al, 2015). These include methods of reducing the axial induction of an upstream turbine and thus also mean and turbulent gradients in the wake (Annoni et al, 2016;Bartl and Saetran, 2016) as well as wake redirection techniques .…”

Section: Introductionmentioning

confidence: 99%

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

2018

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Abstract. In this experimental wind tunnel study the effects of intentional yaw misalignment on the power production and loads of a downstream turbine are investigated for full and partial wake overlap. Power, thrust force and yaw moment are measured on both the upstream and downstream turbine. The influence of inflow turbulence level and streamwise turbine separation distance are analyzed for full wake overlap. For partial wake overlap the concept of downstream turbine yawing for yaw moment mitigation is examined for different lateral offset positions.Results indicate that upstream turbine yaw misalignment is able to increase the combined power production of the two turbines for both partial and full wake overlap. For aligned turbine setups the combined power is increased between 3.5 % and 11 % depending on the inflow turbulence level and turbine separation distance. The increase in combined power is at the expense of increased yaw moments on both the upstream and downstream turbine. For partial wake overlap, yaw moments on the downstream turbine can be mitigated through upstream turbine yawing. Simultaneously, the combined power output of the turbine array is increased. A final test case demonstrates benefits for power and loads through downstream turbine yawing in partial wake overlap. Yaw moments can be decreased and the power increased by intentionally yawing the downstream turbine in the opposite direction.

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“…One of the first studies of this kind was performed by Steinbuch et al [4] They proposed a concept of downrating the power output from upwind turbines in a farm, so that the wind speed in their wake would be higher, leading to higher energy extraction by downwind turbines and possibly an overall increase of power extraction. Later, many other studies followed this approach [5][6][7][8][9]. Unfortunately, in most of these studies, validation of the controllers in experiments or high fidelity turbulence-resolving flow simulations (such as LES) were impossible.…”

Section: Introductionmentioning

confidence: 99%

Optimal Coordinated Control of Power Extraction in LES of a Wind Farm with Entrance Effects

2016

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Abstract:We investigate the use of optimal coordinated control techniques in large eddy simulations of wind farm boundary layer interaction with the aim of increasing the total energy extraction in wind farms. The individual wind turbines are considered as flow actuators, and their energy extraction is dynamically regulated in time, so as to optimally influence the flow field. We extend earlier work on wind farm optimal control in the fully-developed regime (Goit and Meyers 2015, J. Fluid Mech. 768, 5-50) to a 'finite' wind farm case, in which entrance effects play an important role. For the optimal control, a receding horizon framework is employed in which turbine thrust coefficients are optimized in time and per turbine. Optimization is performed with a conjugate gradient method, where gradients of the cost functional are obtained using adjoint large eddy simulations. Overall, the energy extraction is increased 7% by the optimal control. This increase in energy extraction is related to faster wake recovery throughout the farm. For the first row of turbines, the optimal control increases turbulence levels and Reynolds stresses in the wake, leading to better wake mixing and an inflow velocity for the second row that is significantly higher than in the uncontrolled case. For downstream rows, the optimal control mainly enhances the sideways mean transport of momentum. This is different from earlier observations by Goit and Meyers (2015) in the fully-developed regime, where mainly vertical transport was enhanced.

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Survey of wind farm control—power and fatigue optimization

Cited by 203 publications

References 43 publications

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

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

Optimal Coordinated Control of Power Extraction in LES of a Wind Farm with Entrance Effects

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