A tutorial on control-oriented modeling and control of wind farms

Boersma, Sjoerd; Doekemeijer, Bart; Gebraad, Pieter; Fleming, Paul; Annoni, Jennifer; Scholbrock, Andrew; Frederik, Joeri; Wingerden, J. van

doi:10.23919/acc.2017.7962923

Cited by 158 publications

(151 citation statements)

References 128 publications

(162 reference statements)

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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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“…A more elaborate description on control-oriented 30 models in general, see (Boersma et al, 2017). The comparatively low computational complexity of the steady-state models makes it possible to perform optimization algorithms and validations on the basis of large datasets.…”

Section: Wake Deflection Modelmentioning

confidence: 99%

Robust active wake control in consideration of wind direction variability and uncertainty

Rott¹,

Doekemeijer²,

Seifert³

et al. 2018

Preprint

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Abstract. The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability of current environmental conditions in the field and the uncertainty of their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with 5 wind direction measurements recorded at an onshore met mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines.Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimise the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that 10 the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.

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“…The most commonly used surrogate models in wind farm control are steady-state models, which are heuristic and neglect all temporal dynamics (Boersma et al, 2017a). Thus, wind farm control algorithms synthesized using such models neglect any transient dynamics in the wind farm, thereby potentially limiting performance.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, wind farm control algorithms synthesized using such models neglect any transient dynamics in the wind farm, thereby potentially limiting performance. While some of these models have shown success in wind tunnel tests (e.g., Schreiber et al, 2017) and field tests (e.g., Fleming et al, 2017a, b) for power maximization, the actuation frequency is limited to the minutes-scale, since the flow and turbine dynamics are predicted on the minute-scale. Furthermore,5 time-ahead predictions with these models are limited to the time-invariant steady-state, limiting their use for APC.…”

Section: Introductionmentioning

confidence: 99%

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) at its core, 5 which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately two orders of magnitude faster. Using 10 the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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A tutorial on control-oriented modeling and control of wind farms

Cited by 158 publications

References 128 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

Robust active wake control in consideration of wind direction variability and uncertainty

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

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