 Wind turbine power curve modeling using maximum likelihood estimation methodSeokho Seo, Si-Doek Oh and Ho-Young Kwak Renewable Energy, 2019, vol. 136, issue C, 1164-1169 Abstract: Modeling of wind turbine power curve which shows the relationship between wind speed and its power output can be used as an important tool in monitoring and forecasting wind energy. A data-driven approach to find most probable probability distribution function (PDF) for wind speed and turbine power is presented in this study. Equations for the scale and shape parameters in the Weibull wind speed distribution and equations for the four parameters in the logistic function were obtained explicitly by maximum likelihood estimation (MLE) method. With help of a selected data set from the wind speed and the corresponding power output data which was collected over a period of a year, the values of the parameters were obtained by solving the equations by iteration procedures. The predicted powers by the obtained logistic function closely follow the measured turbine powers averaged at 5-min or 10-min. Monitoring turbine power output by the logistic function was also tested for the measured powers in other time duration. Keywords: Logistic function; Wind turbine power curve; Weibull distribution; Maximum likelihood estimation method (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:136:y:2019:i:c:p:1164-1169 DOI: 10.1016/j.renene.2018.09.087 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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