Some Aspects of Wind Power Statistics

Hennessey, Joseph P.

doi:10.1175/1520-0450(1977)016<0119:saowps>2.0.co;2

Cited by 311 publications

(135 citation statements)

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“…The parent distributions of Type I extremes (i.e., with k = 0) include the Weibull distribution. Since it is widely accepted that the Weibull probability density function is a good model for wind speed distributions (Hennessey, 1977), extremes of wind speeds are often modelled by Type I (Cook, 1985). Immediately, the calculation of the quantiles is simplified.…”

Section: A Graphical Methods To Find the Parameters Of The Gumbel Distmentioning

confidence: 99%

A review of methods to calculate extreme wind speeds

Palutikof

Brabson

Lister

et al. 1999

Meteorological Applications

326

209

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High wind speeds pose a threat to the integrity of structures, particularly those at exposed sites such as bridges, wind turbines and radio masts. In any design project for large structures, safety considerations must be balanced against the additional cost of 'over-design'. Accurate estimation of the occurrence of extreme wind speeds is an important factor in achieving the correct balance. Such estimates are commonly expressed in terms of the quantile value X T , i.e., the maximum wind speed which is exceeded, on average, once every T years, the return period. Typically, for most users of wind data, estimates of the 50-year return period gust or wind speed are required, based on 10-20 (and often fewer) available years of observations. For this situation, the data are generally fitted to a theoretical distribution in order to calculate the quantiles. Fisher & Tippett (1928) showed that if a sample of n cases is chosen from a parent distribution, and the maximum (or minimum) of each sample is selected, then the distribution of the maxima (or minima) approaches one of three limiting forms as the size of the samples increases. Gumbel (1958) argued that, in the case of floods, each year of record constitutes a sample with 365 cases and that the annual extreme flood is the maximum value of the sample. Thus, the Fisher-Tippett distributions could be fitted to the set of annual maxima. This is the basis of all classical extreme value theory. The aim is to define the form of the limiting distribution and estimate the parameters, so that values of X T can be calculated.Here, we review and summarize the portion of the extensive literature on extreme value theory relevant to the analysis of wind and gust speed data. The review is carried out with reference to the requirements of a user seeking to select and apply a method appropriate to a particular real data set. We discuss first the available techniques and then the choices required for successful selection and implementation. The methods are organized broadly according to the length of the time series of observations available for analysis, beginning with those which require longer data sets, and moving towards those developed for use with short-duration series. The length of the observational data set is often a problem for those wishing to calculate extreme wind speed quantiles but is a common concern for users of geophysical data, and a substantial literature exists on estimation of extremes from short time series.Note that we do not consider here the matter of anemometer exposure. As with all analyses of wind data, the results of an extreme value analysis will be flawed if the data on which they are based are taken from an anemometer with a non-standard exposure (e.g., sheltered in one or more directions, or at a height above the ground different from the 10 m standard).Meteorol. Appl. 6, 119-132 (1999) A review of methods to calculate extreme wind speeds

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Section: A Graphical Methods To Find the Parameters Of The Gumbel Distmentioning

confidence: 99%

A review of methods to calculate extreme wind speeds

Palutikof

Brabson

Lister

et al. 1999

Meteorological Applications

326

209

View full text Add to dashboard Cite

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“…Justus and Mikhail, 1976;Hennessey, 1977;Pavia and O'Brien, 1986;Wei, 2010). The function can be expressed as follows:…”

Section: Wind Speed Distributionmentioning

confidence: 99%

Multi-scale variability of winds in the complex topography of southwestern Norway

Jonassen¹,

Ólafsson

Reuder³

et al. 2012

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Multi-scale variability of winds in the complex terrain of southwestern Norway is investigated using up to 20 yr of observations from nine automatic weather stations and reanalysis data. Significant differences between the large-and local-scale winds are found. These differences are mainly governed by the large-scale topography of Southern Norway. Winds from the southeast and statically stable flow from the northwest are found to be significantly reduced at the ground level due to large-scale wake and blocking effects. Southwesterly and northeasterly winds are orographically enhanced. At a local scale, there are differences in the wind speed distributions between the surface stations, both in space and time. These differences can to a large extent be quantified in terms of the Weibull distribution function and associated with the respective geographical locations as discretised in four characteristic surface categories: offshore, inland, coast and mountain. The inland category is found to be associated with relatively low but variable wind speeds, whereas the coastal and offshore locations are dominated by more steady and stronger winds. The mountain wind speed distribution is fundamentally different from the others; it shares the variability with the inland locations but the higher average wind speed with the other categories.

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“…The wind kinetic energy (WK) available to be converted into electricity can be described by the following function (Hennessey, 1977;Gunturu and Schlosser, 2012):…”

Section: Methodsologymentioning

confidence: 99%

Efficiency of wind power production and its determinants

Pieralli

Ritter

Odening

2015

Energy

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This article examines the efficiency of wind energy production. We quantify production losses in four wind parks across Germany for 19 wind turbines with non-convex efficiency analysis. In a second stage regression, we adapt the linear regression results of Kneip, Simar, Wilson (2014) to explain electricity losses by means of a bias-corrected truncated regression. Our results show that electricity losses amount to 27% of the maximal producible electricity. These losses can be mainly traced back to changing wind conditions while only 6 % are caused by turbine errors.

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Some Aspects of Wind Power Statistics

Cited by 311 publications

References 0 publications

A review of methods to calculate extreme wind speeds

A review of methods to calculate extreme wind speeds

Multi-scale variability of winds in the complex topography of southwestern Norway

Efficiency of wind power production and its determinants

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