Determination of extreme wind values using the Gumbel distribution

Kang, Dongbum; Ko, Kyungnam; Huh, Jongchul

doi:10.1016/j.energy.2015.03.126

Cited by 45 publications

(25 citation statements)

References 16 publications

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“…Thus the use of the convex hull evokes concepts from extreme value theory [77,78]. The most widely encountered distribution in extreme value theory, the Gumbel distribution [79,80], has been frequently employed for climate modeling, including extreme rainfall and flooding [81][82][83][84][85], extreme winds [86], avalanches [87], and earthquakes [88]. The Gumbel distribution has also been found to reasonably characterize the density fluctuations within galaxies [89][90][91] and in certain areas of tokamaks [92][93][94], binding energies in liquids [95], as well as turbulent fluctuations [96,97].…”

Section: Results: Extreme-value Statistics Of Turbulent Particle Dispmentioning

confidence: 99%

Extreme-value statistics from Lagrangian convex hull analysis for homogeneous turbulent Boussinesq convection and MHD convection

et al. 2017

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We investigate the utility of the convex hull of many Lagrangian tracers to analyze transport properties of turbulent flows with different anisotropy. In direct numerical simulations of statistically homogeneous and stationary Navier-Stokes turbulence, neutral fluid Boussinesq convection, and MHD Boussinesq convection a comparison with Lagrangian pair dispersion shows that convex hull statistics capture the asymptotic dispersive behavior of a large group of passive tracer particles. Moreover, convex hull analysis provides additional information on the sub-ensemble of tracers that on average disperse most efficiently in the form of extreme value statistics and flow anisotropy via the geometric properties of the convex hulls. We use the convex hull surface geometry to examine the anisotropy that occurs in turbulent convection. Applying extreme value theory, we show that the maximal square extensions of convex hull vertices are well described by a classic extreme value distribution, the Gumbel distribution. During turbulent convection, intermittent convective plumes grow and accelerate the dispersion of Lagrangian tracers. Convex hull analysis yields information that supplements standard Lagrangian analysis of coherent turbulent structures and their influence on the global statistics of the flow.

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Section: Results: Extreme-value Statistics Of Turbulent Particle Dispmentioning

confidence: 99%

Extreme-value statistics from Lagrangian convex hull analysis for homogeneous turbulent Boussinesq convection and MHD convection

et al. 2017

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“…Figure 6 represents the annually extreme wind speed and the return period of extreme wind speed values at different heights. Gumbel distribution function is applied to compute the value of extreme wind speed (EWS) [46][47][48]; annually extreme values for Gumbel curve fit are obtained using 2-maxima wind speed values in the individual year at relevant height. The 2-maxima extreme values are ranged from approximately 20 to 30 m/s at four heights.…”

Section: Extreme Wind Speed and Direction Analysismentioning

confidence: 99%

Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric

et al. 2020

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The integration of wind power as an alternative energy source has gotten much attention globally. In this paper, the Weibull distribution model based on different artificial intelligent algorithms and numerical methods is used to evaluate the wind profile. The application of Weibull distribution in wind data assessment can be extensively found, but the methods applied for estimating the parameters still need improvement. Three artificial intelligent algorithms are presented as an alternative method for estimation of Weibull parameters, and an objective function is proposed through the concept of maximum distance metric. Its convergence was proven mathematically through its boundedness for all wind data types. The optimization methods based on the proposed objective function are compared with the conventional numerical approaches for Weibull parameter estimation. Two-year wind data from the site in the southern area of Pakistan has been used to conduct this analysis. Furthermore, this work provides an eloquent way for the selection of a suitable area, evaluation of parameters, and appropriate wind turbine models through real-time data for power production.

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“…The Fisher–Tippett–Gnedenko theorem was applied considering the hourly mean wind speed as an independent random variable. One example of the use of this theorem for periods even smaller than an hour can be found in Kang et al (2015). As a consequence, the monthly maximum of the hourly mean wind-speed-probability distribution function belongs to one of the three types of extreme value distributions.…”

Section: Generalized Extreme Value Distributionmentioning

confidence: 99%

An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

2019

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The aim of this study was to generate hourly mean monthly maximum wind speed return period curves for heights 60 m above the ground using Weather Research and Forecasting modeled data. The methodology introduced produces long-term wind speed data in places where the available measured series for such heights are not long enough for a correct extreme value analysis. Climate Forecast System Reanalysis data are used as input for the Weather Research and Forecasting simulations, providing information for the period 1979–2015. The modeled results are compared with wind speed series measured in anemometric towers, available for the period 2008–2015. Weather Research and Forecasting output is then adjusted to model properly the wind speed at the measuring sites. A good representation of the cumulative distribution of monthly maxima was reached after applying a double linear adjustment.

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Determination of extreme wind values using the Gumbel distribution

Cited by 45 publications

References 16 publications

Extreme-value statistics from Lagrangian convex hull analysis for homogeneous turbulent Boussinesq convection and MHD convection

Extreme-value statistics from Lagrangian convex hull analysis for homogeneous turbulent Boussinesq convection and MHD convection

Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric

An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

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