Open AccessIncorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy
Author(s) -
Pandit Ravi Kumar,
Infield David,
Carroll James
Publication year - 2019
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2285
Subject(s) - density of air , turbine , curve fitting , wind power , power (physics) , power density , gaussian , gaussian process , wind speed , ram air turbine , control theory (sociology) , engineering , computer science , mathematics , meteorology , statistics , artificial intelligence , mechanical engineering , electrical engineering , physics , quantum mechanics , control (management)
A power curve conventionally represents the relationship between hub height wind speed and wind turbine power output. Power curves facilitate the prediction of power production at a site and are also useful in identifying the significant changes in turbine performance which can be vital for condition monitoring. However, their accuracy is significantly influenced by changes in air density, mainly when the turbine is operating below rated power. A Gaussian process (GP) is a nonparametric machine learning approach useful for power curve fitting. Critical analysis of temperature correction is essential for improving the accuracy of wind turbine power curves. The conventional approach is to correct the data for air density before it is binned to provide a power curve, as described in the IEC standard. In this paper, four different possible approaches of air density correction and its effect on GP power curve fitting model accuracy are explored to identify whether the traditional IEC approach used for air density correction is most effective when estimating power curves using a GP. Finding the most accurate air density compensation approach is necessary to minimize GP model uncertainty.