The space-time structure of turbulence for lidar-assisted wind turbine control

Guo, Feng; Mann, Jakob; Peña, Alfredo; Schlipf, David; Cheng, Po‐Wen

doi:10.1016/j.renene.2022.05.133

Cited by 13 publications

(15 citation statements)

References 36 publications

(88 reference statements)

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“…We use the extended Mann turbulence model [6] to generate 1 evolving four-dimensional turbulence fields. With this simulation method, Taylor's frozen hypothesis is avoided to simulate a more realistic lidar wind preview.…”

Section: Turbulencementioning

confidence: 99%

“…Therefore, we use these parameters for stochastic turbulence generation. As for turbulence evolution-related parameters, the parameters summarized by [6] from a neutral atmosphere are chosen. As for the last energy level-related parameter αε 2/3 , it is adjusted based on the hubheight mean wind speed V hub to ensure the standard deviation of longitudinal velocity component (u) equals to that specified by the IEC 61400-3-1:2019 standard [9].…”

Section: Turbulencementioning

confidence: 99%

“…Further, a notch filter is necessary because the activation of blade pitch near the natural frequency of platform pith mode is undesired. The detailed methods of deriving the low-pass filter parameter have been discussed in [6,5]. As for the notch filter, the cutoff frequency is placed at the natural frequency of the platform pith mode.…”

Section: Feedforward Pitchmentioning

confidence: 99%

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The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

Guo,

Schlipf,

Lemmer

et al. 2023

J. Phys.: Conf. Ser.

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In this paper, we analyze the performances of two control strategies and their combination on a floating turbine through OpenFAST simulations. The floating wind turbine is modeled based on the demonstrative FLOATGEN, which consists of a 2MW wind turbine mounted on the Damping Pool platform designed by BW Ideol. The lidar-assisted control utilizes lidar wind preview to achieve blade pitch feedforward control. The multi-variable feedback additionally uses the platform pitch rate to determine blade pitch. By simulations with the above-rated wind and irregular wave conditions, both control strategies and their combination has the potential to reduce turbine vibrations. Especially, combining lidar-assisted control and multi-variable feedback control brings the most significant reduction in the standard deviations of rotor speed (>36%), low-speed shaft torque (>30%), and blade root moment (>15%).

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Section: Turbulencementioning

confidence: 99%

Section: Turbulencementioning

confidence: 99%

Section: Feedforward Pitchmentioning

confidence: 99%

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The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

Guo,

Schlipf,

Lemmer

et al. 2023

J. Phys.: Conf. Ser.

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“…Knowledge of the turbulent wind field in the atmospheric boundary layer (ABL) has great value in many fields, including wind energy (Iungo, Wu & Porté-Agel 2013), weather forecasting (Sun, Flicker & Lilly 1991), pollution dispersion (Nguyen & Soulhac 2021) and wind loading of structures (Guo et al 2022), among others. Over the past decade, pulsed light detection and ranging (lidar) has demonstrated the ability to provide instantaneous measurements of the turbulent wind field over a large area that can extend to several kilometres (Peña et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Large-eddy simulation-based reconstruction of turbulence in a neutral boundary layer using spectral-tensor regularization

Alreweny,

Vandewalle,

Meyers

2024

J. Fluid Mech.

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We propose an efficient method to reconstruct the turbulent flow field in a neutrally stratified atmospheric boundary layer using large-eddy simulation (LES) and a series of lidar measurements. The reconstruction is formulated as a strong four-dimensional variational data assimilation problem, which involves optimizing two competing terms that contribute in the objective functional. The first term is a likelihood term, while the second contains the initial background distribution of turbulent velocity fluctuations and works as a regularization term. However, computing and storing the full background covariance tensor in turbulent flows is time consuming and resource intensive. In the current work, we investigate the possibility of replacing the complex background tensor by simple analytical approximations based on spectral tensors such as the Hunt–Graham–Wilson (HGW) model (Boundary-Layer Meteorol., vol. 85, 1997, pp. 35–52) or the Mann model (J. Fluid Mech., vol. 273, 1994, pp. 141–168). Afterwards, the problem is solved using a quasi-Newton algorithm and preconditioned to enhance the convergence rate. We test the method using virtual lidar measurements collected on a fine reference LES. Results show a super-linear convergence rate of the optimization algorithm to a local minimum and very good agreement between virtual lidar measurements and reconstruction in the scanning region. Furthermore, we demonstrate that incorporating the Saffman energy spectrum ( $E(k) \sim k^2$ where E is the energy spectrum and k is the magnitude of the wavenumber vector) at low wavenumbers into the Mann spectral tensor yields a longer streamwise correlation length, resulting in reduced reconstruction error when compared with the Batchelor spectrum ( $E(k) \sim k^4$ ). Finally, we observe that using the HGW model or Mann model with a Saffman spectrum yields similar results.

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“…We consider both continuous-wave and pulsed Doppler lidars. Based on the four-dimensional (4D) Mann turbulence model that considers wind evolution (Guo et al, 2022a), we optimize the focus distance and the cone angle of the spinner-mounted single-beam lidar to achieve the highest coherence between the rotor-and the lidar-estimated REWS. Then, through time-domain simulations using the 4D Mann turbulence fields with typical turbulence parameters of near-neutral atmospheric stability conditions, the performance of the feedforward control using the optimized lidar is evaluated.…”

Section: Introductionmentioning

confidence: 99%

Feedforward pitch control for a 15-MW wind turbine using a spinner-mounted single-beam lidar

Fu,

Guo,

Schlipf

et al. 2023

Preprint

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Abstract. Feedforward blade pitch control is one of the most promising lidar-assisted control strategies due to its significant improvement in rotor speed regulation and fatigue load reduction. A high-quality preview of the rotor-effective wind speed is a key element to control benefits. In this work, a single-beam continuous-wave or a pulsed lidar system is simulated in the spinner of a bottom-fixed IEA 15 MW wind turbine. The single-beam lidar can rotate with the wind turbine rotor and scan the inflow with a circular pattern, which mimics a multiple-beam nacelle lidar at a lower cost. Also, the spinner-based lidar has an unimpeded view of the inflow without intermittent blockage from the rotating blade. The focus distance and the cone angle of the spinner-based single-beam lidar are optimized for the best wind preview quality based on a rotor-effective wind speed coherence model. Then, the control benefits of using the optimized spinner-based lidar are evaluated for an above-rated wind speed in OpenFAST with an embedded lidar simulator and virtual four-dimensional Mann turbulence fields considering the wind evolution. Results are compared against those using a single-beam nacelle-based lidar. We found that the optimum scanning configurations of both CW and pulsed spinner-based single-beam lidars lead to a lidar scan radius of 0.6 of the rotor radius. Also, results show that a single-beam lidar mounted in the spinner brings much more control benefits (i.e., better rotor speed regulations and higher reductions of the damage equivalent loads on the tower base and blade roots) than the one based on the nacelle. The spinner-based single-beam lidar brings similar performance as a 4-beam nacelle lidar when used for feedforward control.

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The space-time structure of turbulence for lidar-assisted wind turbine control

Cited by 13 publications

References 36 publications

The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

Large-eddy simulation-based reconstruction of turbulence in a neutral boundary layer using spectral-tensor regularization

Feedforward pitch control for a 15-MW wind turbine using a spinner-mounted single-beam lidar

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