Journal article
Monitoring Wind Turbine Vibration Based on SCADA Data
Journal of solar energy engineering, Vol.134(2), 021004
05/01/2012
DOI: 10.1115/1.4005753
Abstract
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.
Details
- Title: Subtitle
- Monitoring Wind Turbine Vibration Based on SCADA Data
- Creators
- Zijun Zhang - Department of Mechanical and Industrial Engineering, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242–1527Andrew Kusiak - Department of Mechanical and Industrial Engineering, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242–1527
- Resource Type
- Journal article
- Publication Details
- Journal of solar energy engineering, Vol.134(2), 021004
- Publisher
- ASME
- DOI
- 10.1115/1.4005753
- ISSN
- 0199-6231
- eISSN
- 1528-8986
- Language
- English
- Date published
- 05/01/2012
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984064216602771
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