Reference wind speed distributions and height profiles for wind turbine design and performance evaluation applications. [USA]

Justus, C. G.; Hargraves, W. R.; Mikhail, A. S.

doi:10.2172/7216073

Cited by 27 publications

(20 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It accounts for the meteorological influence of both wind speed and wetness of the surface soil particles, as well as overcoming a threshold wind speed for surface particles. The wind speed probability density function may be expressed as Weibull distribution (Justus et al, 1976;Apt, 1976):…”

Section: (Zrp=p)mentioning

confidence: 99%

Wind erosion climatic erosivity

Skidmore

1986

Climatic Change

View full text Add to dashboard Cite

A physically based wind-erosion climatic factor has been derived: CE = pf [,~2 _ (~. + .H/pa2)]3/2/(,,)d,~where p is the air density, a is a constant made up of other constants (von Karman, height of wind speed observation, roughness parameter), u is the horizontal wind speed, uT is threshold wind speed, f(u) is a wind speed probability density function, and .~1 is the cohesive resistance caused by water on the soil particles. Cohesive resistance is proportional to the square of water content relative to water content at -1500 J kg-1. Relative water content is approximated from the Budyko dryness ratio and the Thornthwaite PE index with similar results. CE is calculable from wind speed and other generally available meteorological data, and is usable in the wind erosion equation without some of the limitations of a previously used wind erosion climatic factor.

show abstract

Section: (Zrp=p)mentioning

confidence: 99%

Wind erosion climatic erosivity

Skidmore

1986

Climatic Change

View full text Add to dashboard Cite

show abstract

“…C. Cliff et al (1977) proposed a technique for classification of wind resources using just the information of annual wind speed [21]. The hypothesis used in the mentioned study have been established by Justus et al [22] considering that mean wind speed with values higher than 4.5 m/s tends to have Rayleigh distribution function. Based on these assumptions, the annual mean of the cube of wins speed (U 3 ) can be estimated using the following equation.…”

Section: Wind Energy Resource Atlasmentioning

confidence: 99%

“…The functional range of widely used wind turbines is defined with a cut-in (3.5 m/s) and cut-out (25 m/s) wind speed. Those limits are based according to a mechanical characteristics and safety reasons for the turbine's blades [22]. In order to evaluate the inter-seasonal variability of the wind field, we first remove the annual cycle from the data.…”

Section: Wind Energy Resource Atlasmentioning

confidence: 99%

A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean

et al. 2016

View full text Add to dashboard Cite

Renewable energy resources, such as wind, are available worldwide. Locating areas with high and\ud continual wind sources are crucial in pre-planning of wind farms. Vast offshore areas are characterized\ud by higher and more reliable wind resources in comparison with continental areas. However, offshore\ud wind energy production is in a quite preliminary phase. Elaborating the potential productivity of wind\ud farms over such areas is challenging due to sparse in situ observations. The Mediterranean basin is not an\ud exception. In this study we are proposing numerical simulations of near-surface wind fields from\ud regional climate models (RCMs) in order to obtain and fill the gaps in observations over the Mediterranean\ud basin. Four simulations produced with two regional climate models are examined here. Remote\ud sensing observations (QuikSCAT satellite) are used to assess the skill of the simulated fields. A technique\ud for estimating the potential energy from the wind fields over the region is introduced. The wind energy\ud potential atlas and the map of a wind turbine's functional range are presented, locating the potentially\ud interesting sub-regions for wind farms. The ability of models to reproduce the annual cycle and the\ud probability density function of wind speed anomalies are detailed for specified sub-regions

show abstract

“…Most often the wind speed at a meteorology station is measured along a tower at different elevations, and it is desired to be able to find the wind profile at this station for further wind loadings or energy calculations. Some researchers have employed the Weibull pdf for empirical wind speed relative frequency distribution (histogram), and a set of formulas are derived for the extrapolation of the Weibull pdf parameters [15][16][17][18]. In general, two-parameter Weibull pdf of wind speed, P(V ), is given as,…”

Section: Weibull Distribution Parameter Extrapolationmentioning

confidence: 99%

Wind Velocity Vertical Extrapolation by Extended Power Law

Şen

Altunkaynak

Erdik

2012

Advances in Meteorology

View full text Add to dashboard Cite

Wind energy gains more attention day by day as one of the clean renewable energy resources. We predicted wind speed vertical extrapolation by using extended power law. In this study, an extended vertical wind velocity extrapolation formulation is derived on the basis of perturbation theory by considering power law and Weibull wind speed probability distribution function. In the proposed methodology not only the mean values of the wind speeds at different elevations but also their standard deviations and the cross-correlation coefficient between different elevations are taken into consideration. The application of the presented methodology is performed for wind speed measurements at Karaburun/Istanbul, Turkey. At this location, hourly wind speed measurements are available for three different heights above the earth surface.

show abstract

Reference wind speed distributions and height profiles for wind turbine design and performance evaluation applications. [USA]

Cited by 27 publications

References 1 publication

Wind erosion climatic erosivity

Wind erosion climatic erosivity

A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean

Wind Velocity Vertical Extrapolation by Extended Power Law

Contact Info

Product

Resources

About