A Statistical Model for the Prediction of Wind-Speed Probabilities in the Atmospheric Surface Layer

Abstract

Wind fields in the atmospheric surface layer (ASL) are highly three-dimensional and characterized by strong spatial and temporal variability. For various applications such as wind comfort assessments and structural design, an understanding of potentially hazardous wind extremes is important. Statistical models are designed to facilitate conclusions about the occurrence probability of wind speeds based on the knowledge of low-order flow statistics. Being particularly interested in the upper tail regions we show that the statistical behavior of near-surface wind speeds is adequately represented by the Beta distribution. By using the properties of the Beta probability density function in combination with a model for estimating extreme values based on readily available turbulence statistics, it is demonstrated that this novel modelling approach reliably predicts the upper margins of encountered wind speeds. The model's basic parameter is derived from three substantially different calibrating datasets of flow in the ASL originating from boundary-layer wind-tunnel measurements and direct numerical simulation. Evaluating the model based on independent field observations of nearsurface wind speeds showed a high level of agreement between the statistically modelled horizontal wind speeds and measurements. The results show that, based on the knowledge of only a few simple flow statistics (mean wind speed, wind speed fluctuations and integral time scales), the occurrence probability of velocity magnitudes at arbitrary flow locations in the ASL can be estimated with a high degree of confidence.

Current gust forecasting techniques, developments and challenges

“…Suomi et al (2013Suomi et al ( , 2015Suomi et al ( , 2016Suomi et al ( , 2017 characterise gust profiles using measurements and pursue the development of gust parametrisations in this context. Efthimiou et al (2017b, a) model the underlying wind distribution giving rise to gusts as extreme values, using direct numerical simulation (DNS) and wind tunnel measurements to inform and validate the model, which can be applied at any height, also testing at field sites. Consideration of gust profile is already commonplace in the engineering discipline.…”

“…They then constructed relationships between Weibull distribution parameters for extremes of the (sustained) wind and those for extreme gusts, so that synthetic gusts could be obtained at further sites reporting only sustained wind, enabling a more comprehensive gust return period analysis using a 10-year dataset. Others include Hewston and Dorling (2011), Thorarinsdottir and Johnson (2012), Cheng et al (2012aCheng et al ( , 2014, , , Efthimiou et al (2017b), Efthimiou et al (2017a).…”

The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation

“…So, an accurate wind speed distribution modeling is the first step to achieve accurate wind energy production estimation. Moreover, as discussed in [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], the randomness of WS also has a great impact on the mechanical reliability of wind power systems, since extreme values of wind speed (EWS) may damage sensible components of the structures, such as towers and wind blades, so that EWS characterization also constitutes a basic tool for an efficient wind turbine design. Furthermore, as also pointed out in [15][16][17][18][19], values of WS that are greater than the "cut-off" value of the wind generator are generally undesirable, since the electric generator has to be disconnected from the wind turbine to avoid damages to the electrical section of the wind power system; consequently, the "cut off" value of the generator must be chosen according to the characterization of EWS in the particular location, since it has a great impact on aggregate power production [1][2][3][4][21][22][23].…”

Large Eddy Simulation study on the structure of turbulent flow in a complex city

“…Answering this question is of great importance as the field of practical application is shifting its focus to predictions of more specialized variables like extreme values (e.g. for peak concentrations or wind gusts, Efthimiou et al, 2017b;2017c).…”