 Monitoring of wind farms’ power curves using machine learning techniquesAntonino Marvuglia and Antonio Messineo Applied Energy, 2012, vol. 98, issue C, 574-583 Abstract: The estimation of a wind farm’s power curve, which links the wind speed to the power that is produced by the whole wind farm, is a challenging task because this relationship is nonlinear and bounded, in addition to being non-stationary due for example to changes in the site environment and seasonality. Even for a single wind turbine the measured power at different wind speeds is generally different than the rated power, since the operating conditions on site are generally different than the conditions under which the turbine was calibrated (the wind speed on site is not uniform horizontally across the face of the turbine; the vertical wind profile and the air density are different than during the calibration; the wind data available on site are not always measured at the height of the turbine’s hub). Keywords: Wind farm; Power curve; Data-driven; Neural network; Machine learning (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:appene:v:98:y:2012:i:c:p:574-583 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2012.04.037 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.