The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms

Frederik, Joeri; Doekemeijer, Bart; Mulders, Sebastiaan Paul; Wingerden, Jan‐Willem van

doi:10.1002/we.2513

Cited by 75 publications

(127 citation statements)

References 37 publications

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“…CC BY 4.0 License. may significantly increase power production (Munters and Meyers, 2018;Frederik et al, 2019Frederik et al, , 2020 and require future field experimentation.…”

Section: Introductionmentioning

confidence: 99%

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

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

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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.

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“…CC BY 4.0 License. may significantly increase power production (Munters and Meyers, 2018;Frederik et al, 2019Frederik et al, , 2020 and require future field experimentation.…”

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

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“…The blades of the reference turbine are equipped with the four airfoils FFA-W3-241, FFA-W3-301, FFA-W3-360 and FFA-W3-480 (Fuglsang et al, 1998), respectively from tip to root. For the operating conditions analyzed in this paper, the chord-based Reynolds of the G1 varies along the blade span within the range 60 000-85 000.…”

Section: Full-scale Wind Turbinementioning

confidence: 99%

How realistic are the wakes of scaled wind turbine models?

Wang¹,

Campagnolo²,

Canét³

et al. 2021

Wind Energ. Sci.

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Abstract. The aim of this paper is to analyze to which extent wind tunnel experiments can represent the behavior of full-scale wind turbine wakes. The question is relevant because on the one hand scaled models are extensively used for wake and farm control studies, whereas on the other hand not all wake-relevant physical characteristics of a full-scale turbine can be exactly matched by a scaled model. In particular, a detailed scaling analysis reveals that the scaled model accurately represents the principal physical phenomena taking place in the outer shell of the near wake, whereas differences exist in its inner core. A large-eddy simulation actuator-line method is first validated with respect to wind tunnel measurements and then used to perform a thorough comparison of the wake at the two scales. It is concluded that, notwithstanding the existence of some mismatched effects, the scaled wake is remarkably similar to the full-scale one, except in the immediate proximity of the rotor.

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“…The downstream turbine is situated 5 rotor diameters (5D) behind the upstream turbine. The full domain as well as inflow characteristics and turbulence intensity used for the CFD simulation are specified in [34]. The turbine used for the simulation is the DTU 10MW turbine [35], with a rotor diameter of 178.…”

Section: Simulationmentioning

confidence: 99%

On the potential of reduced order models for wind farm control: a Koopman dynamic mode decomposition approach

Cassamo¹,

Wingerden²

2020

Preprint

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The high dimensions and governing non linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non linearities. The work here presented applies the ideas motivated by non linear dynamical systems theory - the Koopman Operator - to an innovative algorithm in the context of wind farm systems - Input Output Dynamic Mode Decomposition - to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

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The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms

Cited by 75 publications

References 37 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

How realistic are the wakes of scaled wind turbine models?

On the potential of reduced order models for wind farm control: a Koopman dynamic mode decomposition approach

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