Evaluation of wind energy potential and estimation of wind turbine characteristics for two different sites

Chaurasiya, Prem Kumar; Kumar, Vimanyu; Warudkar, Vilas; Ahmed, Siraj

doi:10.1080/01430750.2019.1611634

Cited by 20 publications

(10 citation statements)

References 41 publications

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“…To achieve this, we collected and processed wind data on the various sites considered. The analysis allows the production of annual climate statistics at the level of the stations [19]. It emerges in particular that in N'Djamena, the wind has the mean speed and energy density at 10 m from the ground, 3.8m/s and 99W/m 2 respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The Weibull function makes it possible to characterize the frequency distribution of wind speeds over a given period. However, for the analysis of the wind potential of a site, this distribution used for a period of one year or more [12,16,19]. The distribution's equation describes the parameters of the Weibull probability density function of wind speed and follows the shape of a 1/2 "Gaussian" and are the same as those of the CDF.…”

Section: Probability Density Functionmentioning

confidence: 99%

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Estimate of the Wind Resource of Two Cities in the Sahara and Sahel in Chad

Tahir¹,

Abdraman²,

Mouangue³

et al. 2020

IJEPE

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The objective of this work is to map the wind resource of two cities of Ndjamena and Faya located respectively in Chadian Sahara and Sahel. As well as the evaluation of the annual energy produced at an altitude of 80m or 100m by the simulation of wind data from wind speed data collected from metrological stations in the Sahara and Sahel areas respectively in the cities of Ndjamena and Faya Airport, and available for five years. In this perspective, we start with the description of the site, the determination of the various wind parameters, such as the mathematical modeling of the wind frequency distribution: Weibull distribution and the numerical simulation of the real wind data collected over five years to provide predictions on sites in two cities of Ndjamena and Faya. Then, we estimate its wind potential, its prediction of the electrical energy produced and we carry out an analysis of the wind turbulence by taking into account the obstacles surrounding near the measurement mast of Ndjamena and Faya, the topography, the roughness of the site, the density and a judicious choice of wind turbines.

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Section: Discussionmentioning

confidence: 99%

Section: Probability Density Functionmentioning

confidence: 99%

Estimate of the Wind Resource of Two Cities in the Sahara and Sahel in Chad

Tahir¹,

Abdraman²,

Mouangue³

et al. 2020

IJEPE

View full text Add to dashboard Cite

show abstract

“…In addition, estimating wind performance and assessment of the wind energy potential in wind energy planning projects using the twoparameter Weibull was not suitable for large-scale wind energy generation [10]. Three-parameter generalized gamma distribution [30] and normal distribution [31] were found to more accurately describe the wind characteristics compared to the two-parameter Weibull at different locations. Kollu et al [32] developed a wind speed model based on Rayleigh and Weibull distributions.…”

Section: Introductionmentioning

confidence: 97%

Analysis of wind speed characteristics using different distribution models in Medan City, Indonesia

Suwarno

Yusuf

Irwanto

et al. 2021

IJPEDS

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<span lang="EN-CA">Estimating wind speed characteristics plays an essential role in designing a wind power plant at a selected location. In this study, the Weibull, gamma, and exponential distribution models were proposed to estimate and analyze the wind speed parameters and distribution functions. Real measured data were collected from Medan City, Indonesia. The scale and shape factors of wind distribution for three years data were calculated. The observed cumulative probability of the three models was compared to predicted wind characteristics. The probability density function (PDF) and the cumulative density function (CDF) of wind speed were also analyzed. The results showed that the Weibull model was the best model to determine PDF, while the exponential model was the best model to determine CDF for the Medan City wind site.</span>

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“…Estimating wind performance in the WE planning project with the assistance of W2, an assessment of the WE potential in Kudat and Labuan in 2006-2008 resulted in the highest monthly and annual average WS's, so this site is not suitable for large-scale WE generation (Islam et al, 2011). Three parameters of the Generalized Gamma distribution were found to more accurately describe the wind characteristics compared to W2 at different locations (Chaurasiya et al, 2019). Using a generalized extreme distribution to study wind energy variation and its potential in Debuncha, Southwestern Cameroon (Arreyndip et al, 2016).…”

Section: Introductionmentioning

confidence: 98%

The Probability Density Function for Wind Speed Using Modified Weibull Distribution

Suwarno

Zambak²

2021

IJEEP

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Wind speed (WS) is important information to determine the potential for wind energy in an area. Wind speed has been widely expressed by the probability density function (Pdf), one of which uses the Weibull Distribution (WD). Not all WS data can be analyzed by WD because some deficiencies need to be corrected. Modified Weibull Distribution (MWD) is proposed to improve the existing WD models. In addition, this paper also compares the performance of MWD against WD using WS data measured in Medan City. To validate the two models (WD and MWD), the coefficient of determination (R-squared) and the mean square root error (RMSE) were used. In addition, data validation tests were also carried out using Chi-square and Kolmogorov-Smirnov. The result obtained is that MWD has a more acceptable fit than WD for this case.

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Evaluation of wind energy potential and estimation of wind turbine characteristics for two different sites

Cited by 20 publications

References 41 publications

Estimate of the Wind Resource of Two Cities in the Sahara and Sahel in Chad

Estimate of the Wind Resource of Two Cities in the Sahara and Sahel in Chad

Analysis of wind speed characteristics using different distribution models in Medan City, Indonesia

The Probability Density Function for Wind Speed Using Modified Weibull Distribution

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