Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)

Pishgar-Komleh, Seyyed Hassan; Keyhani, Alireza; Sefeedpari, Paria

doi:10.1016/j.rser.2014.10.028

Cited by 250 publications

(135 citation statements)

References 49 publications

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“…Besides applying mean wind speed, a root cube wind speed was applied to calculate the wind power and energy density. Since the wind power is proportional to cube of wind speed, it is a better representation of wind speed to be considered in calculations [19]. The average annual temperature in Jomsom is 13.5˚C.…”

Section: Site Location and Data Collectionmentioning

confidence: 99%

“…According to the literature, for neutral stable condition, m is approximately 0.143, which is commonly assumed to be constant in wind resource assessments. In this research, the surface roughness (m) is taken as 0.143 [19].…”

Section: Vertical Extrapolation Of Wind Speedmentioning

confidence: 99%

“…Another method is to calculate the wind power density using frequency distribution functions like Weibull distribution, Rayleigh distribution, chisquared distribution, generalized normal, log normal-distribution, three parameter log-normal, gamma distribution, inverse Gaussian distribution, kappa, wakeby, normal two variable distributions, normal square root of wind speed distribution, as well as hybrid distribution [19] [21]. Researches have shown that Weibull function fits the wind probability distribution more accurately compared to others [22].…”

Section: Wind Speed Probability Distributionmentioning

confidence: 99%

“…Weibull predicted it to be 9.07 m/s whereas Rayleigh predicted it as 11.5 m/s. Mean speed predicted by weibull was same as predicted main speed because same equation (6,19) are used to calculate value of shape factor and mean speed. Rayleigh model predicted mean wind speed with maximum deviation of 0.37 m/s with 80% of difference not over 0.1 m/s.…”

Section: Weibull and Rayleigh Distributionmentioning

confidence: 99%

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A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

Parajuli¹

2016

EPE

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Section: Site Location and Data Collectionmentioning

confidence: 99%

Section: Vertical Extrapolation Of Wind Speedmentioning

confidence: 99%

Section: Wind Speed Probability Distributionmentioning

confidence: 99%

Section: Weibull and Rayleigh Distributionmentioning

confidence: 99%

See 2 more Smart Citations

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

Parajuli¹

2016

EPE

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“…In this paper, the long-term AWS is used to represent a wind resource, which has the following two advantages: (i) a one-parameter distribution of wind speed can be readily derived from the AWS to represent the approximate variation of wind conditions at the concerned site; and (ii) the resource strength of the site can be readily related to a wind map that represents the wind resources over a region in terms of their estimated AWS. In the context of the first advantage (stated above), it is important to note that both the Weibull and Rayleigh distribution are known to provide an acceptable description of wind speed probability [22][23][24][25] (although the former is more popular and, in general, more accurate). The Rayleigh distribution is a one-parameter model, and we exploit this characteristic to derive an approximate wind speed distribution from the "average wind speed" information; where a unique value of the distribution parameter can be determined from a given value of the mean of the distribution.…”

Section: Geographical Variation Of Wind Patternsmentioning

confidence: 99%

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

et al. 2016

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Abstract:The suitability of turbine configurations to different wind resources has been traditionally restricted to considering turbines operating as standalone entities. In this paper, a framework is thus developed to investigate turbine suitability in terms of the minimum cost of energy offered when operating as a group of optimally-micro-sited turbines. The four major steps include: (i) characterizing the geographical variation of wind regimes in the onshore U.S. market; (ii) determining the best performing turbines for different wind regimes through wind farm layout optimization; (iii) developing a metric to quantify the expected market suitability of available turbine configurations; and (iv) exploring the best tradeoffs between the cost and capacity factor yielded by these turbines. One hundred thirty one types of commercial turbines offered by major global manufacturers in 2012 are considered for selection. It is found that, in general, higher rated power turbines with medium tower heights are the most favored. Interestingly, further analysis showed that "rotor diameter/hub height" ratios greater than 1.1 are the least attractive for any of the wind classes. It is also observed that although the "cost-capacity factor" tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar.

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A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm

Gani

Mohammadi

Shamshirband

et al. 2015

Env Prog and Sustain Energy

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A new hybrid approach by integrating the support vector machine (SVM) with firefly algorithm (FFA) is proposed to estimate shape (k) and scale (c) parameters of the Weibull distribution function according to previously established analytical methods. The extracted data of two widely successful methods utilized to compute parameters k and c were used as learning and testing information for the SVM-FFA method. The simulations were performed on both daily and monthly scales to draw further conclusions. The performance of SVM-FFA method was compared against other existing techniques to demonstrate its efficiency and viability. The results conclusively indicate that SVM-FFA method provides further precision in the predictions. Nevertheless, for daily estimations, the applicability of proposed method could not be feasible owing to high day-by-day fluctuations of parameters k, whereas the results of monthly estimation are completely appealing and precise. In summary, the SVM-FFA is a highly viable and efficient technique to estimate wind speed distribution on monthly scale. It is expected that the proposed method would be profitable for wind researchers and experts to be used in many practical applications, such as evaluating the wind energy potential and making a proper decision to nominate the optimal wind turbines. V C 2015 American Institute of Chemical Engineers Environ Prog, 35: 867-875, 2016

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Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)

Cited by 250 publications

References 49 publications

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm

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