Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data.
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Research Papers
Monitoring Wind Turbine Vibration Based on SCADA Data
Zijun Zhang,
Zijun Zhang
Department of Mechanical and Industrial Engineering,
The University of Iowa
, 3131 Seamans Center, Iowa City, IA 52242–1527
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Andrew Kusiak
Andrew Kusiak
Department of Mechanical and Industrial Engineering,
andrew-kusiak@uiowa.edu
The University of Iowa
, 3131 Seamans Center, Iowa City, IA 52242–1527
Search for other works by this author on:
Zijun Zhang
Department of Mechanical and Industrial Engineering,
The University of Iowa
, 3131 Seamans Center, Iowa City, IA 52242–1527
Andrew Kusiak
Department of Mechanical and Industrial Engineering,
The University of Iowa
, 3131 Seamans Center, Iowa City, IA 52242–1527andrew-kusiak@uiowa.edu
J. Sol. Energy Eng. May 2012, 134(2): 021004 (12 pages)
Published Online: February 27, 2012
Article history
Received:
January 24, 2011
Revised:
December 6, 2011
Online:
February 27, 2012
Published:
February 27, 2012
Citation
Zhang, Z., and Kusiak, A. (February 27, 2012). "Monitoring Wind Turbine Vibration Based on SCADA Data." ASME. J. Sol. Energy Eng. May 2012; 134(2): 021004. https://doi.org/10.1115/1.4005753
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