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Research Papers

Monitoring Wind Turbine Vibration Based on SCADA Data

[+] Author and Article Information
Zijun Zhang, 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 134(2), 021004 (Feb 27, 2012) (12 pages) doi:10.1115/1.4005753 History: Received January 24, 2011; Revised December 06, 2011; Published February 27, 2012; Online February 27, 2012

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.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Evaluation of the number of clusters k in monitoring drivetrain acceleration

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Figure 2

Clustering results from monitoring drivetrain acceleration

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Figure 3

Relationship between values of wind speed and drivetrain acceleration at normal conditions

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Figure 4

Evaluation of k in monitoring tower acceleration

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Figure 5

Visualization of clustering result of monitoring tower acceleration

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Figure 6

Relationship between normal values of wind speed and tower acceleration

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Figure 7

Predicted and observed values of the drivetrain acceleration for the first 100 test points

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Figure 8

Predicted and observed values of tower acceleration for the first 100 test points

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Figure 9

The control chart for the dataset that contains normal drivetrain acceleration data

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Figure 10

The control chart for the dataset that contains some abnormal drivetrain acceleration data

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Figure 11

The control chart for the dataset that contains normal tower acceleration data

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Figure 12

The control chart for the dataset that contains some abnormal tower acceleration data

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Figure 13

Layout design of six wind turbines

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Figure 14

Predicted and observed values of drivetrain acceleration for the first 100 test points

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Figure 15

Predicted and observed values of tower acceleration for the first 100 test points

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Figure 16

The control chart for the dataset that contains normal drivetrain acceleration data

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Figure 17

The control chart for the dataset that contains abnormal drivetrain acceleration data

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Figure 18

The control chart for the dataset that contains normal tower acceleration data

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Figure 19

The control chart for the dataset that contains some abnormal tower acceleration data

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