Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt

Saleh, H.; Aly, A. Abou El-Azm; Abdelhady, Salama

doi:10.1016/j.energy.2012.05.021

Cited by 132 publications

(52 citation statements)

References 14 publications

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“…Meanwhile, in this work, the MLM and MMLM methods resulted in the second and third worst performances. In comparison with other studies [17][18][19]21], the MLM and MMLM methods showed better performances for estimating Weibull parameters, which was against the result of this work. Since wind speed data less than 0.5 m/s were not used to estimate Weibull parameters in this work, that seems to have affected the performance of the estimation.…”

Section: Impact Of Topographical Conditions On the Performance Of Thesupporting

confidence: 69%

“…The different points compared with existing studies are as follows: firstly, more actual wind speed data for the longer-term period of time were analyzed to improve the reliability of the results following each method's accuracy test. The measurement datasets in one to five sites covering the period of one to three years were usually analyzed in the previous studies [13][14][15][16][17][18][19][20][21], while the datasets in nine sites for five years were analyzed in this investigation. Secondly, although the topographical conditions were known as a major factor influencing wind conditions, it has been little discussed in terms of the accuracy of the methods.…”

Section: Introductionmentioning

confidence: 99%

“…On the whole, the MLM performed best. Saleh et al [19] used observation data from a wind farm in Egypt and studied five numerical methods to determine the most appropriate one: the empirical, MLM, MMLM, graphical, and energy pattern factor methods. They used the root mean square error (RMSE) test to compare the accuracy of each method.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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On Jeju Island, South Korea, an investigation was conducted to determine the best method for estimating Weibull parameters. Six methods commonly used in many fields of the wind energy industry were reviewed: the empirical, moment, graphical, energy pattern factor, maximum likelihood, and modified maximum likelihood methods. In order to improve the reliability of a research result, five-year actual wind speed data taken from nine sites with various topographical conditions were used for the estimation. Furthermore, the effect of various topographical conditions on the accuracy of the methods was analyzed and 10 bin interval types were applied to determine the most appropriate bin interval based on their performances. Weibull distributions that were estimated using these methods were compared with the observed wind speed distribution. Then the accuracy of each method was evaluated using four accuracy tests. The results showed that of the six methods, the moment method had the best performance regardless of topographical conditions, while the graphical method performed the worst. Additionally, topographical conditions did not affect the accuracy ranking of the methods for estimating the Weibull parameters, while an increase of terrain complexity resulted in an increase of discrepancy between the estimated Weibull distribution and the frequency of the observed wind speed data. In addition, the choice in bin interval greatly affected the accuracy of the graphical method while it did not depend on the accuracy of the modified maximum likelihood method.

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Section: Impact Of Topographical Conditions On the Performance Of Thesupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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“…In this paper a comparison is made between the theoretically calculated maximum entropy probability density function and the estimated probability density function using the Weibull distribution function [24][25][26][27]. The goodness-of-fit tests for the maximum entropy cumulative distribution function with the cumulative distribution of the measured wind speed data are also performed.…”

Section: Weibull Distribution Function and Goodness-of-fit Testsmentioning

confidence: 99%

Comparison of Wind Energy Generation Using the Maximum Entropy Principle and the Weibull Distribution Function

et al. 2016

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Proper knowledge of the wind characteristics of a site is of fundamental importance in estimating wind energy output from a selected wind turbine. The present paper focuses on assessing the suitability and accuracy of the fitted distribution function to the measured wind speed data for Baburband site in Sindh Pakistan. Comparison is made between the wind power densities obtained using the fitted functions based on Maximum Entropy Principle (MEP) and Weibull distribution. In case of MEP-based function a system of (N+1) non-linear equations containing (N+1) Lagrange multipliers is defined as probability density function. The maximum entropy probability density functions is calculated for 3-9 low order moments obtained from measured wind speed data. The annual actual wind power density (P A ) is found to be 309.25 W/m 2 while the Weibull based wind power density (P W ) is 297.25 W/m 2 . The MEP-based density for orders 5, 7, 8 and 9 (P E ) is 309.21 W/m 2 , whereas for order 6 it is 309.43 W/m 2 . To validate the MEP-based function, the results are compared with the Weibull function and the measured data. Kolmogorov-Smirnov test is performed between the cdf of the measured wind data and the fitted distribution function (Q 95 = 0.01457 > Q = 10 −4 ). The test confirms the suitability of MEP-based function for modeling measured wind speed data and for the estimation of wind energy output from a wind turbine. R 2 test is also performed giving analogous behavior of the fitted MEP-based pdf to the actual wind speed data (R 2~0 .9). The annual energy extracted using the chosen wind turbine based on Weibull function is P W = 2.54 GWh and that obtained using MEP-based function is P E = 2.57-2.67 GWh depending on the order of moments.

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“…A larger value of " " indicates more spread of wind power [29]. Therefore, this study adopted standard deviation method, which is expressed in [25,30] …”

Section: Computation Of Wind Energymentioning

confidence: 99%

On the Wind Energy Resource and Its Trend in the East China Sea

Osinowo

Lin

Zhao

et al. 2017

Journal of Renewable Energy

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This study utilizes a 30-year (1980-2009) 10 m wind field dataset obtained from the European Center for Medium Range Weather Forecast to investigate the wind energy potential in the East China Sea (ECS) by using Weibull shape and scale parameters. The region generally showed good wind characteristics. The calculated annual mean of the wind power resource revealed the potential of the region for large-scale grid-connected wind turbine applications. Furthermore, the spatiotemporal variations showed strong trends in wind power in regions surrounding Taiwan Island. These regions were evaluated with high wind potential and were rated as excellent locations for installation of large wind turbines for electrical energy generation. Nonsignificant and negative trends dominated the ECS and the rest of the regions; therefore, these locations were found to be suitable for small wind applications. The wind power density exhibited an insignificant trend in the ECS throughout the study period. The trend was strongest during spring and weakest during autumn.

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Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt

Cited by 132 publications

References 14 publications

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Comparison of Wind Energy Generation Using the Maximum Entropy Principle and the Weibull Distribution Function

On the Wind Energy Resource and Its Trend in the East China Sea

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