Mean Wind Power Density Research Articles

Characteristics of the available wind energy in Bahrain

The wind characteristics and the available wind energy in Bahrain has been studied. The mean long-term wind speed and its variation at a height of 10 m above ground level were found to be 4.90 and 0.823 ms −1 respectively. Accordingly, the annual mean wind power density is 69.2 ± 0.34 Wm −2. The size of windmill needed to supply the required domestic power and the number of batteries to provide energy storage for use at a certain continuous rate of energy have been estimated.

The wind energy potential of Malaysia

Routine observations have been used to determine the diurnal and seasonal variations of the wind at 20 meteorological stations in Malaysia. The wind speed distributions, with calms omitted, are represented by Weibull functions. Seasonal mean wind power densities at the surface exceed 20 W m −2 only at stations on the east coast and in the south of West Malaysia during the northeast monsoon from November to March. Estimates of seasonal power densities at 600 m above the surface range from below 100 W m −2 to over 300 W m −2, with the highest values over the south of West Malaysia and the east coasts of East and West Malaysia during the northeast monsoon.

Wind power dependence upon weather systems

SYNOPSIS Discriminant analysis is used to classify synoptic weather regimes at two contrasting locations in southern Africa for the purpose of wind power assessment. Four dominant weather categories at each location are identified and shown to be statistically distinct in terms of mean wind speed, mean wind power density and mean stability. Their differing characteristics and different frequencies of occurrence mean that the synoptic categories make vastly different contributions to total wind power at each of the two stations and, as such, represent means by which estimates of wind power can be extended by the use of numerical models, measurements can be organized into efficient samples and predictions of the surface wind fields can be developed.

Regression equations between mean wind power density and mean hourly wind speed during the monsoon season

Utilising the hourly wind speed data of some selected Indian stations with good wind regime during the monsoon season, mean dally and monthly wind power density and mean daily and monthly wind speeds have been obtained for each day and for each month. From these, correlation coefficients between mean daily power density and cube of mean daily wind speed and also between mean monthly power density and cube of mean monthly wind speed have been computed for the monsoon months. These correlations are found to be consistently very high (> 0.92) and stable. In view of this very close and stable relationship, regression equations have been obtained between the mean daily wind power density and the cube of mean daily wind speed, as well as between the mean monthly wind power density and the cube of mean monthly wind speed during the monsoon season. The constants in these equations are found to have a linear relationship with the mean wind speed. These equations were applied to independent data to estimate the mean wind power density for the monsoon season from the mean wind speed and the estimates for the different stations were generally found to be within 15 percent of the mean power for the season as computed from the hourly wind speed data for the independent period. The equations have the potential of application to mean wind, speed from the cup anemometer data which are available from a large number of stations in India, for estimation of mean wind power density during the monsoon season.

The Use of the Weibull Three-Parameter Model for Estimating Mean Wind Power Densities

Abstract The Weibull three-parameter model is discussed for estimation of mean wind power densities. This probability density function is a generalization of a number of more conventional density functions. Using wind speed observations, it is shown that this model generally gives a more reliable fit to the empirical wind speed frequency data than the density functions with one or two parameters. Wind power density estimations turn out to be strongly dependent on the hypothesized probability density function. The variation with height of the three parameters of the discussed model is investigated; no simple height dependence can be proposed.

On the Vertical Extrapolation of Mean Wind Power Density

A simple method of estimating the height variation of the mean wind power density is presented which accounts for the variation of the exponent p of the wind speed power law, with stability and roughness. Measurements are used to compare the new method to two previously suggested methods. The results indicate that the presently suggested method gives the most accurate estimate of the extrapolated mean wind power densities.

On the Use of Power Laws for Estimates of Wind Power Potential

The evaluation of wind power potential at a proposed aerogenerator site by extrapolation from measured winds at a reference level is investigated. It is shown that the total mean wind power density is not particularly sensitive to the selection of roughness length or power law exponent; over the entire likely range of these parameters the wind power in the mean flow at typical aerogenerator hub heights is within 1.4–4 times the power at a reference height of about 10 m. In lieu of in situ profile measurements, it is suggested that a power law exponent of 1/7 is adequate for realistic but conservative estimates of the available wind power except at extremely rough sites where the estimates may only be conservative.

Some Aspects of Wind Power Statistics

Abstract Some of the problems of wind power statistics are examined. The exact relationship between the mean wind speed and the mean of the cube of the wind speed is discussed. The Weibull probability density function, a good model for wind speed distributions, leads to a Weibull model for the distribution of the cube of the wind speed. This model facilitates the computation of the mean and the standard deviation of the total wind power density, the usable wind power density, and the wind power density during the hours when an aerogenerator is operating. The Weibull model is applied to data from three Oregon wind power sites located in rugged terrain. It is concluded that the mean and standard deviation of the wind speed are the minimum statistics necessary for wind power estimates, that the Weibull model for the wind power density has many computational advantages, and that the existing wind power studies based solely on the total mean wind power density omit much valuable information about the wind powe...