Parameterization of wind evolution using lidar

Yiyin Chen,Po Wen Cheng,David Schlipf

doi:10.5194/wes-6-61-2021

Yiyin Chen, Po Wen Cheng + Show 1 more

Open Access

https://doi.org/10.5194/wes-6-61-2021

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Abstract

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.

Highlights

Wind evolution refers to the physical phenomenon of turbulence structures changing over time and is defined, in this study, as magnitude-squared coherence dependent on evolution time
Because turbulent eddies are advected by the mean flow while evolving, the longitudinal coherence, i.e., coherence of turbulent velocity at locations separated in the mean direction of the flow, is used to measure wind evolution in practice
In terms of σ and IT, it is surprising to notice that the Gaussian process regression (GPR) models show a preference for σ rather than IT, IT is more commonly used in data analysis and simulation in wind energy

Summary

Introduction

Wind evolution refers to the physical phenomenon of turbulence structures (eddies) changing over time and is defined, in this study, as magnitude-squared coherence dependent on evolution time. Magnitude-squared coherence (hereafter referred to as coherence) is a common statistical measure of turbulence structure properties (see, e.g., Panofsky and McCormick, 1954; Davenport, 1961; Panofsky et al, 1974). Taylor’s (1938) hypothesis is a special case that assumes all turbulent motions remain unchanged, while eddies move with the mean flow. In other words, it assumes no wind evolution, which means the coherence is unity for all frequencies. The validity of Taylor’s (1938) hypothesis was researched in some studies (see, e.g., Willis and Deardorff, 1976; Schlipf et al, 2011), and this hypothesis is widely used in data analysis and wind field modeling for the sake of simplification (see, e.g., Kelberlau and Mann, 2019; Veers, 1988)

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