Comparative study of numerical methods for determining Weibull parameters for wind energy potential

Arslan, Taner; Bulut, Y. Murat; Yavuz, Arzu Altin

doi:10.1016/j.rser.2014.08.009

Cited by 120 publications

(54 citation statements)

References 25 publications

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“…They found further preciseness for SVR approach in comparison to other soft computing methodologies. Arslan et al (2014) presented the application of L-moment method (L-MoM) to estimate shape and scale parameters. They utilized the Monte Carlo simulation and compared the results with Moment (MoM) and ML methods.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

et al. 2015

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the same Weibull parameters. The survey results reveal that applying ELM approach is eventuated in attaining further precision for estimation of both Weibull parameters compared to other methods evaluated. Mean absolute percentage error, mean absolute bias error and root mean square error for k are 8.4600 %, 0.1783 and 0.2371, while for c are 0.2143 %, 0.0118 and 0.0192 m/s, respectively. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate Weibull k and c factors.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

et al. 2015

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“…The PDF values obtained from the time-series wind data should be compared with the PDF values obtained by using each method. The RMSE is defined [5,18,38]:…”

Section: Rmsementioning

confidence: 99%

“…In particular, it provides a convenient representation of potential wind energy, and it is used to assess the economic feasibility of a potential wind farm [3][4][5]. Thus, it is important to determine how closely the data from Weibull distribution estimates match data from actual wind speed distribution.…”

Section: Introductionmentioning

confidence: 99%

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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“…Traditionally Weibull distribution is widely used to represent wind speed series at a given site [4,[11][12][13]. Normal, Gamma, Lognormal or combination of these distributions with Weibull distribution [4,7,[14][15][16], empirical wavelet [17] or Kernel density method [18] can also be used to model wind speed series.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of wind speed series and power in Durban

Legesse¹,

Saha²,

Carpanen³

2017

J. energy South. Afr.

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Both the planning and operating of a wind farm demand an appropriate wind speed model of its location. The model also helps predict the dynamic behaviour of wind turbines and wind power potential in the location. This study characterises the wind speed series and power in Durban (29.9560°S, 30.9730°E) IntroductionDepletion of fossil fuel reserves, global warming, security concerns, and rising commodity prices are pushing the world to go green. Much attention is currently given to the development of renewable energy, among which harnessing wind energy is the cheapest alternative [1][2][3]. Feasibility studies related to wind power require an appropriate wind speed model of a site [4,5]. These models are also important in planning and operating wind turbines. Hence, the wind speeds of a specified site should be appropriately characterised to determine wind energy potential and attain comprehensive results in the investigations of the dynamics of the wind turbines [4,6,7]. Moreover, a good characterisation of wind speed helps transmission system operators in scheduling their power dispatch [4]. Wind is a random stochastic process whose dynamic behaviour can be represented by a stochastic model [8]. Naturally, it depends on pressure gradient, waves, jet streams, and local weather conditions [9]. Its stochastic modelling is a complicated task because of its strong variability in time and land terrains. Over a year, wind speed is periodic, showing seasonal variations; however, hourly average wind speed is a stochastic process with a Weibull probability density function; whereas within minutes, it follows a Gaussian distribution [10].Different methods have been employed for time series characterisation of wind processes. Traditionally Weibull distribution is widely used to represent wind speed series at a given site [4,[11][12][13]. Normal, Gamma, Lognormal or combination of these distributions with Weibull distribution [4,7,[14][15][16], empirical wavelet [17] or Kernel density method [18] can also be used to model wind speed series. Shokrzadeh et al. [19] and Kazemi and Goudarzi [20] employed advanced parametric and nonparametric and least square approximation methods to forecast wind power. A typical distribution may not necessarily represent the cumulative wind behaviour of all locations in a region [7]. Thus, the wind speed for a particular location needs to be modelled. The above distributions cannot be used, however, when chronology is considered [4]. A rough observation on the raw wind speed data from Durban demonstrated that the current wind speed depended on the previous wind speed, indicating that chronology should be considered in modelling the wind speeds. Evolutionary algorithms such as genetic algorithm and local search technique are also used in wind speed modelling [21] despite their time consuming procedure [4]. The Markov chain model, which retains chronology and consumes less time, could, therefore, be employed to synthesise wind speed time series for dynamic simulation and wind power f...

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Comparative study of numerical methods for determining Weibull parameters for wind energy potential

Cited by 120 publications

References 25 publications

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

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

Characterisation of wind speed series and power in Durban

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