Parameterization of Wind Evolution using Lidar

Chen, Yiyin; Schlipf, David; Cheng, Po Wen

doi:10.5194/wes-2020-50

Cited by 5 publications

(13 citation statements)

References 15 publications

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“…As shown in Table 5, R 2 of all recommended cases ranges from 0.67 to 0.83. These results are much better than that of the preliminary study (Chen, 2019); in particular, the prediction accuracy of the offset parameter b has been significantly improved. This is mainly owing to the use of the ARD-SE kernel, which can help to select predictors reasonably and give different weights to predictors according to their relevant importance for the prediction, whereas kernel functions with a common length scale for predictors were applied in the preliminary study.…”

Section: Model Evaluationcontrasting

confidence: 61%

“…In addition, it is desired to gain some insights into the complex relationships between wind evolution and wind-field-related variables such as wind statistics, atmospheric stability, and relative positions of measurement points. For these purposes, a previous study (Chen, 2019) was done to explore different supervised machine learning algorithms on a simple level, including stepwise linear regression (see, e.g., Hocking, 1976), regression tree (see, e.g., Breiman et al, 1984), support vector regression (see, e.g., Vapnik, 1995), and Gaussian process regression (see, e.g., Rasmussen and Williams, 2006). It was found that Gaussian process regression, overall, performs the best for prediction of wind evolution model parameters, and thus its potential is further analyzed in this study with more extensive data.…”

Section: Y Chen Et Al: Parameterization Of Wind Evolution Using Lidarmentioning

confidence: 99%

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Parameterization of wind evolution using lidar

2021

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Abstract. Wind evolution, i.e., the evolution of turbulence structures over time, has become an increasingly interesting topic in recent years, mainly due to the development of lidar-assisted wind turbine control, which requires accurate prediction of wind evolution to avoid unnecessary or even harmful control actions. Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into standard 3D simulations to provide a more realistic simulation environment for this control concept. Motivated by these factors, this research aims to investigate the potential of Gaussian process regression in the parameterization of wind evolution. Wind evolution is commonly quantified using magnitude-squared coherence of wind speed and is estimated with lidar data measured by two nacelle-mounted lidars in this research. A two-parameter wind evolution model modified from a previous study is used to model the estimated coherence. A statistical analysis is done for the wind evolution model parameters determined from the estimated coherence to provide some insights into the characteristics of wind evolution. Gaussian process regression models are trained with the wind evolution model parameters and different combinations of wind-field-related variables acquired from the lidars and a meteorological mast. The automatic relevance determination squared exponential kernel function is applied to select suitable variables for the models. The performance of the Gaussian process regression models is analyzed with respect to different variable combinations, and the selected variables are discussed to shed light on the correlation between wind evolution and these variables.

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Section: Model Evaluationcontrasting

confidence: 61%

Section: Y Chen Et Al: Parameterization Of Wind Evolution Using Lidarmentioning

confidence: 99%

Parameterization of wind evolution using lidar

2021

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“…(6). A single DWL can be used to study the longitudinal co-coherence (Sjöholm et al, 2010;Davoust and von Terzi, 2016;Cheynet et al, 2017b;Debnath et al, 2020;Chen et al, 2020). Also, the value of the C x as a function of the range gate can provide additional information on the influence of the coastline on the flow characteristics.…”

Section: Co-coherence Estimatesmentioning

confidence: 99%

The COTUR project: Remote sensing of offshore turbulence for wind energy application

Cheynet¹,

Flügge²,

Reuder³

et al. 2021

Preprint

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Abstract. The paper presents the measurement strategy and dataset collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and Lidar Planner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The preliminary results show an undocumented variation of the lateral coherence with the distance from the coast. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines.

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“…It is worth mention that several authors [5], [8] use formulas of the squared coherence. For conformity with the formula in IEC Kaimal model, we use the non-squared formula here.…”

Section: B Wind Modelmentioning

confidence: 99%

“…Sketch of previous and proposed method on wind preview quality estimation the correlation of measurements at two positions within the rotor plane. In LES simulations [5] and real application [8], the turbulence coherence structure varies with atmospheric conditions, such that the lidar wind preview quality changes as well. Previously, the wind preview studies were done mainly with a frequency-based correlation study by analyzing the coherence between REWS of the turbine to the one provided by the lidar [1], [5], [7].…”

Section: Introductionmentioning

confidence: 99%

Lidar Wind Preview Quality Estimation for Wind Turbine Control

Guo

Schlipf

2021

2021 American Control Conference (ACC)

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Lidar-assisted wind turbine control has been proven to be beneficial for wind turbines. This technology uses the preview of rotor-effective wind speed obtained from lidar measurements in front of the wind turbine. The wind preview allows the controller to react to the disturbance prior to the impact on the turbine. However, the quality of the wind preview provided by a lidar is constantly changing mainly with atmospheric conditions and usually only the lower frequency components of the signal can be used for control. Thus, it is necessary to estimate the wind preview quality online and accordingly adjust the filter parameters to remove the uncorrelated information and to schedule controller parameters. Previously, the wind preview quality during short field testing campaigns has been identified offline prior to using the lidar signal for control. This was done with a frequency-based correlation study by comparing the rotor-effective wind speed estimated from turbine data to the one provided by the lidar. An online application of the frequency-based correlation study however is hard to implement due to the limitation of the estimation from turbine data, the amount of data necessary, and the sensitivity of the frequency-based method itself. In this paper, we develop a new statistic-based method to estimate the wind preview quality by only using the data from a pulsed lidar system without the need of wind turbine data. The method is assessed using simulated lidar measurement scanning an unfrozen wind field. The results show that the method is able to distinguish the preview quality under different wind evolution conditions.

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Parameterization of Wind Evolution using Lidar

Cited by 5 publications

References 15 publications

Parameterization of wind evolution using lidar

Parameterization of wind evolution using lidar

The COTUR project: Remote sensing of offshore turbulence for wind energy application

Lidar Wind Preview Quality Estimation for Wind Turbine Control

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