How realistic are the wakes of scaled wind turbine models?

The study presents the methodology, implementation, and first results of the experimental test of a lifetime-aware wind farm control (WFC) approach. The novel strategy goes beyond the conventional WFC formulations and aims to maximise profit while ensuring fulfilment of a desired lifetime. Damage due to fatigue is formulated using a cost model and is also limited by a lifetime constraint. The control strategy is based on yaw-induced wake steering and utilises a tool chain of power and damage estimators. The experiment was conducted with three model turbines in a boundary layer wind tunnel, which allowed the simulation of a realistic dynamic variation of the wind direction. The novel strategy is compared to conventional greedy operation, as well as to the classical maximum power strategy. The results show that the novel control strategy can improve the economic performance of the farm.

Section: Methodsmentioning

confidence: 99%

First experimental results on lifetime-aware wind farm control

Braunbehrens,

Anand,

Campagnolo

et al. 2024

“…The scaled model serves as a validation case of the simulation setup against experimental data, while the larger IEA 3.4 MW is chosen as a representation of modern onshore wind turbines. The ALM by [19] is used by running simulations with an in-house version of SOWFA (Simulator fOr Wind Farm Applications) [20], properly modified to reproduce the aerodynamics of scaled models [21]. In addition, for the IEA 3.4 MW, the filtered version of the ALM is implemented to account for a non-optimal kernel size [22].…”

Section: Set-upmentioning

confidence: 99%

Further improvements to the double-Gaussian wake model

Zengler,

Braunbehrens,

Tamaro

2024

In onshore wind farms, the turbine-to-turbine distance can be in the order of three diameters or less. In these circumstances, traditional wake models, developed for the far wake region, lose accuracy. The double-Gaussian wake model is an approach to extend algebraic wake modelling to the near wake region, where the wake is not yet self-similar. In this work, the double-Gaussian wake model is advanced by performing large eddy simulations of a reference wind turbine and a scaled wind turbine model to investigate how the thrust coefficient and the turbulence intensity influence the near wake and wake recovery. Results show that in the near wake the maximum velocity deficits move towards the outer part of the rotor with an increasing thrust coefficient. Thus, a thrust coefficient dependent position of the double-Gaussian peaks is proposed. In addition, a wake expansion function including turbulence intensity and thrust coefficient is tuned. The new model proves to be capable of predicting the wake of different turbine models under various ambient conditions and operational states.

“…However, the wake dynamics of this model is expected to be similar to the one of the G06 wind turbine used in the experiments, given the same yaw excitation. The choice of this turbine was motivated by the fact that its numerical model had been validated in previous work [22]. All simulations were performed at a constant tip speed ratio 𝜆 𝑜𝑝𝑡 and a fixed blade pitch.…”

Section: Simulation Setupmentioning

confidence: 99%

Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization

Mühle,

Tamaro,

Klinger

et al. 2024

This study investigates open-loop dynamic yaw control as a strategy for enhancing wind farm performance through wake mixing. The focus is on understanding the potential for enhanced wake mixing under different turbulence intensities, and the mechanisms triggering mixing. Experimental tests are conducted using scaled wind tunnel experiments with two model wind turbines. The wake flow is analysed by large eddy simulations (LES). Dynamic yawing is prescribed by sinusoidally varying the yaw angle at different excitation frequencies. The study reveals that dynamic yaw control, particularly at low inflow turbulence, leads to increased wake mixing. The resulting enhanced wind farm power capture has a rather flat maximum around an optimal Strouhal number. This is in contrast to high inflow turbulence, where the effectiveness of the control strategy is significantly reduced. The meandering motion of the wake induced by dynamic yaw excitation is identified as the key mechanism for improved wake recovery.