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Data-driven multivariate power curve modeling of offshore wind turbines
Janssens, O.; Noppe, N.; Devriendt, C.; Van de Walle, R.; Van Hoecke, S. (2016). Data-driven multivariate power curve modeling of offshore wind turbines. Engineering Applications of Artificial Intelligence 55: 331-338. https://hdl.handle.net/10.1016/j.engappai.2016.08.003
In: Engineering Applications of Artificial Intelligence. PERGAMON-ELSEVIER SCIENCE LTD: Oxford. ISSN 0952-1976; e-ISSN 1873-6769, more
Peer reviewed article  

Available in  Authors 

Keyword
    Marine
Author keywords
    Performance monitoring; Condition monitoring; Machine learning; Datamining; Wind energy

Authors  Top 
  • Janssens, O., more
  • Noppe, N.
  • Devriendt, C., more
  • Van de Walle, R., more
  • Van Hoecke, S., more

Abstract
    Performance monitoring of offshore wind turbines is an essential first step in the condition monitoring process. This paper provides three novelties regarding power curve modeling. The first consists of illustrating that univariate power curve modeling can be improved by the use of non-parametric methods such as stochastic gradient boosted regression trees, extremely randomized forest, random forest, K-nearest neighbors, and the method of bins according to the IEC standard 61,400-12-1. This is confirmed on both a synthetic data set and a real live data set containing data from three offshore wind turbines. The second novelty consists of an improvement regarding overall power curve modeling results by the use of multivariate models which incorporate the wind direction, rotations per minute of the rotor, yaw, wind direction and pitch additional to the wind speed. The best improvement is achieved by the stochastic gradient boosted regression trees method for which the mean absolute error can be decreased by up to 27.66%. The third novelty consists of making a synthetic data set available for bench-marking purposes.

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