Open AccessWind turbine power curve estimation based on earth mover distance and artificial neural networks
Author(s) -
Bai Li,
Crisostomi Emanuele,
Raugi Marco,
Tucci Mauro
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.0530
Subject(s) - wind power , artificial neural network , turbine , computer science , cluster analysis , wind speed , curve fitting , power (physics) , radial basis function , control theory (sociology) , artificial intelligence , data mining , engineering , machine learning , meteorology , geography , aerospace engineering , physics , quantum mechanics , electrical engineering , control (management)
A data‐based estimation of the wind–power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data‐based procedure to build a robust and accurate estimate of the wind–power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance‐based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut‐in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance.