Multivariate analysis and prediction of wind turbine response to varying wind field characteristics based on machine learning

Publikationstyp
Conference Paper
Date Issued
2013-06
Sprache
English
Author(s)
Park, Jinkyoo  
Smarsly, Kay  
Law, Kincho H.  
Hartmann, Dietrich  
TORE-URI
http://hdl.handle.net/11420/14231
Start Page
113
End Page
120
Citation
Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering: 113-120 (2013)
Contribution to Conference
ASCE International Workshop on Computing in Civil Engineering, IWCCE 2013  
Publisher DOI
10.1061/9780784413029.015
Scopus ID
2-s2.0-84887322480
Publisher
American Soc. of Civil Engineers
Site-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of wind turbine structures to varying wind field characteristics. Machine learning algorithms are applied (i) to better understand changes of wind field characteristics due to atmospheric conditions, and (ii) to gain new insights into the wind turbine loads being affected by fluctuating wind. Using Gaussian Mixture Models, the variations in wind field characteristics are investigated by comparing the joint probability distribution functions of several wind field features, which are constructed from long-term monitoring data taken from a 500-kW wind turbine in Germany. Furthermore, based on Gaussian Discriminative Analysis, representative daytime and nocturnal wind turbine loads are predicted, compared, and analyzed. © 2013 American Society of Civil Engineers.
DDC Class
600: Technik