Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil

•Six numerical methods are applied for estimating Weibull distribution parameters.•Cluster analysis is applied to verify the similarity of hourly wind speed data.•27 stations in the state of Mato Grosso do Sul, Brazil, were analyzed.•The accuracy of the methods was evaluated by three different stati...

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Veröffentlicht in:Sustainable energy technologies and assessments 2020-12, Vol.42, p.100854, Article 100854
Hauptverfasser: Guarienti, José Antonio, Kaufmann Almeida, Aleska, Menegati Neto, Armando, de Oliveira Ferreira, Ayrton Renan, Ottonelli, João Paulo, Kaufmann de Almeida, Isabel
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Sprache:eng
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Zusammenfassung:•Six numerical methods are applied for estimating Weibull distribution parameters.•Cluster analysis is applied to verify the similarity of hourly wind speed data.•27 stations in the state of Mato Grosso do Sul, Brazil, were analyzed.•The accuracy of the methods was evaluated by three different statistical analysis. Brazil is one of the countries that invests the most in renewable energy generation. The share of wind power sources in the Brazilian Electricity Matrix rose from 0.9% in 2012 to 7.6% in 2018. The wind resource assessment is a crucial step in planning a wind energy project. Weibull distribution function is used to determine wind power potential for the investigated site and the distribution parameters are estimated by numerical methods. This study focus on the similarity and analyses of hourly wind speed data series. In this study, six numerical methods (Graphical Method, Maximum Likelihood Method, Modified Maximum Likelihood Method, Moment Method, Empirical Method and Power Density Method) are applied for estimating Weibull distribution parameters in 27 stations in the state of Mato Grosso do Sul, Brazil. The stations were grouped according to the similarity of their data series, and for each group the data series were separated into data series for calibration and data series for the accuracy analysis of the methods. The accuracy of the methods was evaluated using three different statistical analysis techniques. The Maximum Likelihood Method and the Modified Maximum Likelihood Method were the best methods for analyzing most of the data series in the stations.
ISSN:2213-1388
2213-1396
DOI:10.1016/j.seta.2020.100854