Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments

Frederik, Joeri; Weber, R. J.; Cacciola, Stefano; Campagnolo, Filippo; Croce, Alessandro; Bottasso, Carlo L.; Wingerden, Jan‐Willem van

doi:10.5194/wes-2019-50

Cited by 24 publications

(57 citation statements)

References 14 publications

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“…The Strouhal number is associated with vortex shedding, which has relevance in tower and rotor wake behavior; the Strouhal number has also been recently used to describe the enhanced wake recovery obtained by dynamic induction control (Frederik et al, 2019). A rotor-wake relevant definition of this nondimensional parameter is St = f 2R/V , where f is a characteristic frequency.…”

Section: Wake Aerodynamicsmentioning

confidence: 99%

On the scaling of wind turbine rotors

Canét¹,

Bortolotti²,

Bottasso³

2020

Preprint

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Abstract. This article formulates laws for scaling wind turbine rotors. Although the analysis is general, the article primarily focuses on subscaling, i.e. on the design of a smaller size model mimicking a full-scale machine. The present study considers both the steady-state and transient response cases, including the effects of aerodynamic, elastic, inertial and gravitational forces. The analysis reveals the changes to physical characteristics induced by a generic change of scale, indicates which characteristics can be matched faithfully by a sub-scaled model, and states the conditions that must be fulfilled for desired matchings to hold. Based on the scaling laws formulated here, two different strategies to design scaled rotors are considered: in the first strategy the scaled model is simply geometrically zoomed from the reference full-scale one, while in the second strategy the scaled rotor is completely redesigned in order to match desired characteristics of the full-scale machine. The two strategies are discussed and compared, highlighting their respective advantages and disadvantages. The comparison considers the scaling of a reference 10-MW wind turbine of about 180 m of diameter down to three different sizes of 54, 27 and 2.8 m. Simulation results indicate that, with the proper choices, several key performance indicators can be accurately matched even by models characterized by significant scaling factors.

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Section: Wake Aerodynamicsmentioning

confidence: 99%

On the scaling of wind turbine rotors

Canét¹,

Bortolotti²,

Bottasso³

2020

Preprint

Self Cite

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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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“…Data availability. The data from the wind tunnel experiments presented in this paper can be accessed here: https://doi.org/10.5281/zenodo.3673680 (Frederik et al, 2020).…”

mentioning

confidence: 99%

Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments

Frederik

Weber²,

Cacciola

et al. 2020

Wind Energ. Sci.

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Abstract. As wind turbines in a wind farm interact with each other, a control problem arises that has been extensively studied in the literature: how can we optimize the power production of a wind farm as a whole? A traditional approach to this problem is called induction control, in which the power capture of an upstream turbine is lowered for the benefit of downstream machines. In recent simulation studies, an alternative approach, where the induction factor is varied over time, has shown promising results. In this paper, the potential of this dynamic induction control (DIC) approach is further investigated. Only periodic variations, where the input is a sinusoid, are studied. A proof of concept for this periodic DIC approach will be given by the execution of scaled wind tunnel experiments, showing for the first time that this approach can yield power gains in real-world wind farms. Furthermore, the effects on the damage equivalent loads (DEL) of the turbine are evaluated in a simulation environment. These indicate that the increase in DEL on the excited turbine is limited.

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Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments

Cited by 24 publications

References 14 publications

On the scaling of wind turbine rotors

On the scaling of wind turbine rotors

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

Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments

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