Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters

Ayodele, T.R.; Jimoh, Adisa A.; Munda, Josiah L.; Agee, John T.

doi:10.17159/2413-3051/2012/v23i2a3160

Cited by 48 publications

(31 citation statements)

References 10 publications

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“…Compared to wind speed, the power density of wind is a more reliable parameter in estimating wind power potential of a site. It is expressed as [17]:…”

Section: Wind Power Densitymentioning

confidence: 99%

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Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

Kombe

Muguthu

2019

JENRR

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Wind energy is among the fastest growing energy generation technology which is highly preferred alternative to conventional sources of energy. The major Scottish Government target is to deliver 30% of her energy demand by 2020 from renewable sources of energy as well as meeting the emission targets as set under the Scotland Climate Change Act 2009. In this paper, wind energy potential assessment of Great Cumbrae Island was investigated. For this, a ten year mean monthly wind speed at height 50 m obtained from the National Aeronautic Space Administration (NASA) were analysed using the Weibull probability distributions to assess the wind energy potential of Great Cumbrae Island as a clean, sustainable energy resource. Results from the wind-speed model showed that Great Cumbrae Island as high wind-speed site with a mean wind speed of 7.598 m/s and having power density . The annual energy captured by four selected horizontal wind turbine models was determined. The result shows that GE 2.0 platform can capture 4.5 GWh energy in a year which is an acceptable quantity for wind energy.

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“…Compared to wind speed, the power density of wind is a more reliable parameter in estimating wind power potential of a site. It is expressed as [17]:…”

Section: Wind Power Densitymentioning

confidence: 99%

“…This is the most possible wind speed a given site can experience [14,17]. In a probability distribution function, it is denoted by the peak of the distribution.…”

Section: The Most Frequent Wind Speedmentioning

confidence: 99%

Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

Kombe

Muguthu

2019

JENRR

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“…The constant k is the shape parameter while c is the scale parameter for the Weibull distribution based on the mean wind speed standard deviation approach [5,17]. Knowledge of v pr is fundamental to estimating the potential of the preferred choice of a BEWT in the given environment.…”

Section: Wind Speed Datamentioning

confidence: 99%

“…Wind speed is a random variable; hence, it can be represented statistically, with Weibull distribution being recommended by most authors due to its flexibility, simplicity, and capability to fit a wide range of wind data [5][6][7]. This paper is aimed at using the Weibull distribution function to assess the wind resource potential of Fort Beaufort, South Africa for the purpose of deploying BEWT systems.…”

Section: Introductionmentioning

confidence: 99%

An Assessment of the Wind Power Generation Potential of Built Environment Wind Turbine (BEWT) Systems in Fort Beaufort, South Africa

Manyeredzi

Makaka

2018

Sustainability

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The physical and economic sustainability of using built environment wind turbine (BEWT) systems depends on the wind resource potential of the candidate site. Therefore, it is crucial to carry out a wind resource assessment prior to the deployment of the BEWT. The assessment results can be used as a referral tool for predicting the performance and lifespan of the BEWT in the given built environment. To date, there is limited research output on BEWTs in South Africa, with available literature showing a bias towards utility-scale or conventional ground-based wind energy systems. This study aimed to assess the wind power generation potential of BEWT systems in Fort Beaufort using the Weibull distribution function. The results show that Fort Beaufort wind patterns can be classified as fairly good and that BEWTs can best be deployed at 15 m for a fairer power output as roof height wind speeds require BEWTs of very low cut-in speeds of at most 1.2 ms −1 .

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“…Different types of RES, such as solar, wind, and biomass have been exploited as alternatives to the burning of fossil fuels to generate electric power. In the past two decades, efforts were made on effectively integrating RESs into the grid (Mosetlhe et al, 2017;Ayodele et al, 2012), with wind energy showing great potential in electricity generation. As wind energy depends primarily on the behaviour of wind it has stochastic characteristics, and it is imperative that these are known for a given installation, so that the wind energy is effectively utilised.…”

Section: Introductionmentioning

confidence: 99%

Investigating seasonal wind energy potential in Vredendal, South Africa

Mosetlhe

Yusuff

Hamam

2018

J. energy South. Afr.

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Global warming and the energy crisis have necessitated an urgent exploitation and utilisation of renewable energy. Wind energy has gained popularity over the years because of vast availability of its resource. A study was carried out to investigate the stochastic characteristics of the available wind energy at installation sites. Data for a ten-minute interval wind speed collected over a period of five years and measured at a height of 10, 40 and 62 m in Vredendal was considered. Wind speed data was arranged in seasonal format and its statistical distribution investigated based on Weibull, lognormal and gamma distributions. The Anderson-Darling test and Akaike information criterion were used to evaluate the goodness of fit. The results showed that wind power at different heights and time stamps exhibited different statistical distribution. It was found that wind turbines in Vredendal must be installed as high as pos-sible to harness wind power effectively. During summer and spring, there was a high potential for wind power availability compared with that of winter.

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Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters

Cited by 48 publications

References 10 publications

Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

An Assessment of the Wind Power Generation Potential of Built Environment Wind Turbine (BEWT) Systems in Fort Beaufort, South Africa

Investigating seasonal wind energy potential in Vredendal, South Africa

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