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Research Papers

Analysis of Wind Turbine Vibrations Based on SCADA Data

[+] Author and Article Information
Andrew Kusiak

Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527andrew-kusiak@uiowa.edu

Zijun Zhang

Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527

J. Sol. Energy Eng 132(3), 031008 (Jun 14, 2010) (12 pages) doi:10.1115/1.4001461 History: Received April 10, 2009; Revised January 23, 2010; Published June 14, 2010; Online June 14, 2010

Vibrations of a wind turbine have a negative impact on its performance. Mitigating this undesirable impact requires knowledge of the relationship between the vibrations and other wind turbine parameters that could be potentially modified. Three approaches for ranking the impact importance of measurable turbine parameters on the vibrations of the drive train and the tower are discussed. They include the predictor importance analysis, the global sensitivity analysis, and the correlation coefficient analysis versed in data mining and statistics. To decouple the impact of wind speed on the vibrations of the drive train and the tower, the analysis is performed on data sets with narrow speed ranges. Wavelet analysis is applied to filter noisy accelerometer data. To exclude the impact malfunctions on the vibration analysis, the data are analyzed in a frequency domain. Data-mining algorithms are used to build models with turbine parameters of interest as inputs, and the vibrations of drive train and tower as outputs. The performance of each model is thoroughly evaluated based on metrics widely used in the wind industry. The neural network algorithm outperforms other classifiers and is considered to be the most promising approach to study wind turbine vibrations.

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Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Torque histogram for Data Partition 1 of Turbine 1

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Figure 2

Torque histogram for Data Partition 1 of Turbine 1 after sampling

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Figure 3

Histogram of the blade pitch angle rate of Turbine 1 in Data Partition 1

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Figure 4

Histogram of the blade pitch angle rate of Turbine 2 in Data Partition 1

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Figure 5

Histogram of the blade pitch angle rate of Turbine 1 in Data Partition 2

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Figure 6

Histogram of the blade pitch angle rate of Turbine 2 in Data Partition 2

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Figure 7

Histogram of torque rate of Turbine 1 in Data Partition 3

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Figure 8

Histogram of torque rate of Turbine 2 in Data Partition 3

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Figure 9

Spectrum from 0 Hz to 0.05 Hz of the drive train acceleration of Turbine 1

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Figure 10

Scatter plot of the observed and predicted values of drive train acceleration for the first 200 points of Turbine 1 in Scenario 3

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Figure 11

Scatter plot of the observed and predicted values of tower acceleration for the first 200 points of Turbine 1 in Scenario 3

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Figure 12

Scatter plot of the observed and predicted values of the drive train acceleration for the first 200 points of Turbine 2 in Scenario 3

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Figure 13

Scatter plot of the observed and predicted values of the tower acceleration for the first 200 points of Turbine 2 in Scenario 3

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