Probability models of wind velocity magnitude and persistence

Corotis, Ross B.; Sigl, Arden B.; Klein, Joel D.

doi:10.1016/0038-092x(78)90065-8

Cited by 146 publications

(38 citation statements)

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“…Selection of the right machine for the right site plays a capital role in the success of the project. Depending on the Weibull scale and shape parameters, it is possible to identify the rated speed suitable for a particular wind regime [4,5,13]. Selecting the appropriate machine to a given site leads to …”

Section: Figs 4: Comparison Of Power Curves For Different Rated Wind mentioning

confidence: 99%

“…Most of the researches have indicated Weibull distribution function [1], [2] and Rayleigh distribution function [3], [4] are the most commonly adopted methods to represent wind-speed distributions of various wind farms. Weibull distribution that uses scale parameter and shape parameter to express annual mean wind speed and associated standard deviation may appropriately represent the probability distribution of wind speeds.…”

Section: Introductionmentioning

confidence: 99%

“…Mathew et al [8] presented an analytical approach to study the wind energy density, energy available in the wind spectra, and the energy received by turbine by using the Rayleigh wind speed distribution. Corotis et al [9] preferred the Rayleigh distribution for the wind data. Other authors [10][11][12] also used the Weibull model and found that the wind data can be represented by the Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

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Optimal selection of Wind Turbine Generators

Bencherif¹,

Brahmi²,

Chikhaoui³

2014

IJCA

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This paper examines optimum selection of wind turbines between site and wind turbine generators. An analysis methodology is done at the planning and development stages of installation of wind power stations will enable the wind power developer or the power utilities to make a judicious and rapid choice of suitable wind energy conversion system from the available potential sites. The methodology of analysis is based on the computations of annual capacity factors, which are done using the Weibull distribution function and power curve model. The methodology helps to the determination of the speeds characteristic range of the wind machines and to make easy the choice of the suitable wind turbine for a given site, in order to maximize the delivered energy for a given amount of available wind energy. This methodology is applied to install a wind energy conversion system at four sites in Algeria.

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Section: Figs 4: Comparison Of Power Curves For Different Rated Wind mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal selection of Wind Turbine Generators

Bencherif¹,

Brahmi²,

Chikhaoui³

2014

IJCA

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“…Sathyajith et al (2002) used the Rayleigh distribution to characterise the wind potential at eight sites in the Kerala area of India to determine the available energy over a period of time, along with the most common wind velocities, and the wind velocity with the maximum usable energy. Corotis et al (1978) suggested that the Rayleigh distribution might be more useful than the Weibull distribution, but Henneessy (1977) determined that the energy production calculated from wind velocity using the Rayleigh distribution was 10% of that returned by the Weibull distribution. Rehman et al (1994) used the Weibull distribution to study wind data from 10 sites in Saudi Arabia and concluded that this approach was adequate.…”

Section: Wind Velocity Monthly Distributionmentioning

confidence: 99%

The use of wind pumps for greenhouse microirrigation: A case study for tomato in Cuba

Peillón¹,

Sánchez²,

Tarquis³

et al. 2013

Agricultural Water Management

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Crop irrigation is a major consumer of energy. Only a few countries are self-sufficient in conventional nonrenewable energy sources. Fortunately, there are renewable ones, such as wind, which has experienced recent developments in the area of power generation. Wind pumps can play a vital role in irrigation projects in remote farms.A methodology based on daily estimation balance between water needs and water availability was used to evaluate the feasibility of the most economic windmill irrigation system. For this purpose, several factors were included: three-hourly wind velocity (W 3 h, m/s), flow supplied by the wind pump as a function of the elevation height (H, m) and daily greenhouse evapotranspiration as a function of crop planting date. Monthly volumes of water required for irrigation (Dr, m 3 /ha) and in the water tank (Vd, m 3 ), as well as the monthly irrigable area (Ar, ha), were estimated by cumulative deficit water budgeting taking in account these factors.An example is given illustrating the use of this methodology on tomato crop (Lycopersicon esculentum Mill.) under greenhouse at Ciego de Ávila, Cuba. In this case two different W 3 h series (average and low wind year), three different H values and five tomato crop planting dates were considered. The results show that the optimum period of wind-pump driven irrigation is with crop plating in November, recommending a 5 m 3 volume tank for cultivated areas around 0.2 ha when using wind pumps operating at 15 m of height elevation.

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“…The Weibull distribution function has been shown to have advantages over other distribution functions [8], including flexibility, simplicity and the ability to accommodate a wide range of data [9]. In recent years, much research on the assessment of wind energy potential has been performed around the word, with many of these investigations using wind data obtained at a height of 10 m aboveground level.…”

Section: Introductionmentioning

confidence: 99%

Wind resource assessment and wind power potential for the city of Ardabil, Iran

Fazelpour

Soltani

Rosen

2014

Int J Energy Environ Eng

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The results of an investigation of the potential of wind energy as a power source are reported for an Iranian north-western city, Ardabil. The Weibull probability distribution function using a long-term data source, consisting of 6 years (2005-2010) of 3-h period measured mean wind data, is adopted and analyzed. The data have been measured at a height of 10 m above the ground level. Also, monthly and annual wind speed variations are analyzed. The monthly mean wind speed is between 2.09 and 6.58 m/s, while the annual mean wind speed is in the range of 3.28-4.08 m/s. The Ardabil site is found to have good characteristics from the perspective of mean wind speed. The numerical values of the shape and scale parameters for Ardabil are seen to vary over a wide range. The monthly mean value of shape parameter (k) is between 2.62 and 3.17, while the monthly mean value of the Weibull scale parameter (c) is between 3.36 and 5.43 m/s. The results show that the months of October and September have higher mean power densities and that July has a lower mean power density than other months. Also, the wind potential of the studied region is determined to be very suitable for the off-grid connections and could be acceptable for connecting to power grids.

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Probability models of wind velocity magnitude and persistence

Cited by 146 publications

References 1 publication

Optimal selection of Wind Turbine Generators

Optimal selection of Wind Turbine Generators

The use of wind pumps for greenhouse microirrigation: A case study for tomato in Cuba

Wind resource assessment and wind power potential for the city of Ardabil, Iran

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