Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey

Gökçek, Murat; Bayülken, Ahmet; Bekdemir, Şükrü

doi:10.1016/j.renene.2006.11.017

Cited by 186 publications

(81 citation statements)

References 18 publications

(23 reference statements)

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“…The 2-parameter Weibull distribution (W2) and the Rayleigh distribution (RAY) are the pdfs that are the most commonly used in wind speed data analysis especially for studies related to wind energy estimation [35,27,42,51,45,7,16,23,36,3,2,1,25,40,10,31,43,46]. The W2 is by far the most widely used distribution to characterize wind speed.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions of wind speed in the UAE

Ouarda

Charron²,

Shin³

et al. 2015

Energy Conversion and Management

188

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a b s t r a c tFor the evaluation of wind energy potential, probability density functions (pdfs) are usually used to describe wind speed distributions. The selection of the appropriate pdf reduces the wind power estimation error. The most widely used pdf for wind energy applications is the 2-parameter Weibull probability density function. In this study, a selection of pdfs are used to model hourly wind speed data recorded at 9 stations in the United Arab Emirates (UAE). Models used include parametric models, mixture models and one non-parametric model using the kernel density concept. A detailed comparison between these three approaches is carried out in the present work. The suitability of a distribution to fit the wind speed data is evaluated based on the log-likelihood, the coefficient of determination R 2 , the Chi-square statistic and the Kolmogorov-Smirnov statistic. Results indicate that, among the one-component parametric distributions, the Kappa and Generalized Gamma distributions provide generally the best fit to the wind speed data at all heights and for all stations. The Weibull was identified as the best 2-parameter distribution and performs better than some 3-parameter distributions such as the Generalized Extreme Value and 3-parameter Lognormal. For stations presenting a bimodal wind speed regime, mixture models or nonparametric models were found to be necessary to model adequately wind speeds. The two-component mixture distributions give a very good fit and are generally superior to non-parametric distributions.

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Section: Introductionmentioning

confidence: 99%

Probability distributions of wind speed in the UAE

Ouarda

Charron²,

Shin³

et al. 2015

Energy Conversion and Management

188

View full text Add to dashboard Cite

show abstract

“…The maximum annual mean wind speed recorded was 5.66 m/s in Maden. Further research in the northwestern Marmara region in Turkey was performed [11] and it was found that the higher mean wind speeds occurred during winter, while the lower values appeared in autumn. Furthermore, they indicated that the Weibull shape parameter k ranged between 1.57 in autumn and 2.21 in summer, while the corresponding scale parameter c ranged between 4.56 m/s in autumn and 5.93 m/s in winter, respectively.…”

Section: Introductionmentioning

confidence: 99%

Wind energy potential assessment in Naxos Island, Greece

2010

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“…In this case, the value of parameter k is fixed as 2. The probability and cumulative distribution function can be represented following [30,31] which is given as follows:…”

Section: Weibull Probability Distribution Functionmentioning

confidence: 99%

Technical and economic assessment of wind power potential of Nooriabad, Pakistan

2017

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Background: Pakistan is a developing nation and heavily spends on the development of conventional power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of site using wind speed, wind direction, and other meteorological data collected over a period of 1 year. This type of detailed investigation provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine. Methods: The site-specific air density, wind shear, wind power density, annual energy yield, and capacity factors have been calculated at 30 and 50 m above the ground level (AGL). The Weibull parameters have been calculated using empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), energy pattern (EPFM), and graphical (GM) methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R

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Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey

Cited by 186 publications

References 18 publications

Probability distributions of wind speed in the UAE

Probability distributions of wind speed in the UAE

Wind energy potential assessment in Naxos Island, Greece

Technical and economic assessment of wind power potential of Nooriabad, Pakistan

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