Analyzing the Temporal Variation of Wind Turbine Responses Using Gaussian Mixture Model and Gaussian Discriminant Analysis

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Site-specific and time-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 a wind turbine structure to diurnal and nocturnal wind fields. Machine-learning algorithms are applied (1) to better understand the changes of wind field characteristics because of atmospheric conditions and (2) to gain insights into the wind turbine loads being affected by the wind field. Using a Gaussian mixture model, the variations in wind field characteristics are investigated by comparing the joint probability density functions of selected wind field features. The wind field features are constructed from long-term monitoring data taken from a 500-kW wind turbine in Germany that is used as a reference system. Furthermore, employing Gaussian discriminant analysis, representative daytime and nocturnal wind turbine loads are compared and analyzed.
Publisher
ASCE-AMER SOC CIVIL ENGINEERS
Issue Date
2015-07
Language
English
Article Type
Article
Keywords

FRAMEWORK

Citation

JOURNAL OF COMPUTING IN CIVIL ENGINEERING, v.29, no.4

ISSN
0887-3801
DOI
10.1061/(ASCE)CP.1943-5487.0000416
URI
http://hdl.handle.net/10203/212663
Appears in Collection
IE-Journal Papers(저널논문)
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