A constrained wind farm controller providing secondary frequency regulation: An LES study

Boersma, Sjoerd; Doekemeijer, Bart; Siniscalchi-Minna, Sara; Wingerden, Jan‐Willem van

doi:10.1016/j.renene.2018.11.031

Cited by 61 publications

(67 citation statements)

References 27 publications

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“…While many controllers in the literature have been tested in simulation, they were typically assessed in idealistic conditions, using simplified models [4]. There are only a handful of closed-loop control algorithms that were tested in a high-fidelity wind farm simulation (e.g., [9], [34]- [36]). An important contribution of this work is the facilitation of a communication infrastructure that enables researchers to test their control algorithms more easily in a high-fidelity environment.…”

Section: )mentioning

confidence: 99%

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

Doekemeijer

Wingerden

Fleming

2019

2019 American Control Conference (ACC)

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In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines. One method to reduce wake losses is by misaligning the rotor with the incoming flow using its yaw actuator, thereby laterally deflecting the wake away from downstream turbines. However, this demands an accurate and computationally tractable model of the wind farm dynamics. This problem calls for a closed-loop solution. This tutorial paper fills the scientific gap by demonstrating the full closed-loop controller synthesis cycle using a steady-state surrogate model. Furthermore, a novel, computationally efficient and modular communication interface is presented that enables researchers to straight-forwardly test their control algorithms in large-eddy simulations. High-fidelity simulations of a 9-turbine farm show a power production increase of up to 11% using the proposed closed-loop controller compared to traditional, greedy wind farm operation.

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Section: )mentioning

confidence: 99%

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

Doekemeijer

Wingerden

Fleming

2019

2019 American Control Conference (ACC)

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“…Design problems focus on maximizing wind farm power production and reducing the levelized cost of energy by optimizing wind farm layouts [1][2][3][4] and wind turbine set points for yaw, tilt, and thrust [5,6]. Active control attempts to actuate turbines dynamically to reduce farm level power fluctuations [7], track a power output reference signal to provide power grid services [8][9][10][11], or maximize power production [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Wake Modeling Paradigm for Wind Farm Design and Control

et al. 2019

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Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications.

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“…This parameterisation was further applied by Munters et al [13] and Boersma et al [14] to investigate control strategies for yaw and axial induction in wind farms for power optimisation. In their studies [13,14], the wind turbine power is obtained indirectly from the turbine-induced thrust and the local velocity. Fleming et al [15] applied the ADMR to study the large-scale trailing vortices in the wake behind a yawed wind turbine.…”

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confidence: 99%

Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

Lin

Porté‐Agel

2019

Energies

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In this study, we validated a wind-turbine parameterisation for large-eddy simulation (LES) of yawed wind-turbine wakes. The presented parameterisation is modified from the rotational actuator disk model (ADMR), which takes account of both thrust and tangential forces induced by a wind turbine based on the blade-element theory. LES results using the yawed ADMR were validated with wind-tunnel measurements of the wakes behind a stand-alone miniature wind turbine model with different yaw angles. Comparisons were also made with the predictions of analytical wake models. In general, LES results using the yawed ADMR are in good agreement with both wind-tunnel measurements and analytical wake models regarding wake deflections and spanwise profiles of the mean velocity deficit and the turbulence intensity. Moreover, the power output of the yawed wind turbine is directly computed from the tangential forces resolved by the yawed ADMR, in contrast with the indirect power estimation used in the standard actuator disk model. We found significant improvement in the power prediction from LES using the yawed ADMR over the simulations using the standard actuator disk without rotation, suggesting a good potential of the yawed ADMR to be applied in LES studies of active yaw control in wind farms.Energies 2019, 12, 4574 2 of 18 blade-resolved LES of ABL flows through wind turbines [3]. Therefore, most of the LES studies on wind turbine wakes represent the forces induced by wind turbines in ABL flows with various parameterisations.The standard actuator disk model (ADM) without rotation is the simplest wind turbine parameterisation, in which a wind turbine is modelled as a permeable disk with uniformly-distributed normal thrust forces applied on it [4]. A thrust coefficient C T is required by the standard ADM a priori to determine the magnitude of the thrust force. Wu and Porté-Agel [5] later proposed the rotational ADM parameterisation (ADMR), in which the wind turbine thrust and tangential forces are modelled by the blade-element theory. Using aerodynamic and geometric data of the blade as inputs, the ADMR accounts for the wake rotation and non-uniform force distribution on the wind turbine disk. Comparing to the LES results using the standard ADM, Wu and Porté-Agel [5] found that using the ADMR improves the prediction of the main wake flow statistics, such as the mean streamwise velocity and the turbulence intensity.The actuator line model (ALM) parameterisation, developed by Sørensen and Shen [6], was also applied in previous LES studies of wind turbine wakes (e.g., [7][8][9][10]). The ALM represents each blade of a wind turbine as a rotating line source of body forces and computes the corresponding blade-induced forces along the actuator line dynamically in the simulation. As the ALM computes the forces induced by each wind turbine blade, it can resolve more flow structures in the wake, such as tip vortices, than the standard ADM and the ADMR parameterisations. Using the ALM in LES also requires finer mesh resolution and time ste...

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A constrained wind farm controller providing secondary frequency regulation: An LES study

Cited by 61 publications

References 27 publications

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

A Wake Modeling Paradigm for Wind Farm Design and Control

Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models

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