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International Conference on Condition Monitoring of Machinery in Non-Stationary Operation

CMMNO 2016: Advances in Condition Monitoring of Machinery in Non-Stationary Operations pp 223–233Cite as

Operational Condition Monitoring of Wind Turbines Using Cointegration Method

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Part of the book series: Applied Condition Monitoring ((ACM,volume 9))

Abstract

This paper presents a cointegration-based method for condition monitoring and fault detection of wind turbines. The proposed method is based on the residual-based control chart approach. The main idea is that cointegration is a property of some sets of nonstationary time series where a linear combination of the nonstationary series can produce a stationary residual. Then the stationarity of cointegration residuals can be used in a control chart as a potentially effective damage feature. The method is validated using the experimental data acquired from a wind turbine drivetrain with a nominal power of 2 MW under varying environmental and operational conditions. Two known abnormal problems of the wind turbine are used to illustrate the fault detection ability of the method. A cointegration-based procedure is performed on six process parameters of the wind turbine where data trends have nonlinear characteristics. Analysis of cointegration residuals—obtained from cointegration process of wind turbine data—is used for operational condition monitoring and fault/abnormal detection. The results show that the proposed method can effectively monitor the wind turbine and reliably detect abnormal problems.

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References

  1. Kusiak, A., & Li, W. (2011). The prediction and diagnosis of wind turbine faults. Renewable Energy, 36(1), 16–23.

    Article  Google Scholar 

  2. Hameed, Z., Hong, Y. S., Cho, Y. M., Ahn, S. H., & Song, C. K. (2009). Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 13(1), 1–39.

    Article  Google Scholar 

  3. Garcia Marquez, F. P., Tobias, A. M., Pinar Perez, J. M., & Papaelias, M. (2012). Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 46, 169–178.

    Article  Google Scholar 

  4. Zaher, A., McArthur, S. D. J., Infield, D. G., & Patel, Y. (2009). Online wind turbine fault detection through automated SCADA data analysis. Wind Energy, 12(6), 574–593.

    Article  Google Scholar 

  5. Qiu, Y., Feng, Y., Tavner, P., Richardson, P., Erdos, G., & Chen, B. (2012). Wind turbine SCADA alarm analysis for improving reliability. Wind Energy, 15(8), 951–966.

    Article  Google Scholar 

  6. Yang, W., Court, R., & Jiang, J. (2013). Wind turbine condition monitoring by the approach of SCADA data analysis. Renewable Energy, 53, 365–376.

    Article  Google Scholar 

  7. Schlechtingen, M., Santos, I. F., & Achiche, S. (2013). Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing, 13(1), 259–270.

    Article  Google Scholar 

  8. Feng, Y., Qiu, Y., Crabtree, C. J., Long, H., & Tavner, P. J. (2013). Monitoring wind turbine gearboxes. Wind Energy, 16(5), 728–740.

    Article  Google Scholar 

  9. Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error-correction: Representation, estimation and testing. Econometrica, 55, 251–276.

    Article  MathSciNet  Google Scholar 

  10. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231–254.

    Article  MathSciNet  Google Scholar 

  11. Chen, Q., Kruger, U., & Leung, A. Y. T. (2009). Cointegration testing method for monitoring non-stationary processes. Industrial and Engineering Chemistry Research, 48, 3533–3543.

    Article  Google Scholar 

  12. Cross, E. J., Worden, K., & Chen, Q. (2011). Cointegration: A novel approach for the removal of environmental trends in structural health monitoring data. Proceedings of the Royal Society A, 467, 2712–2732.

    Article  Google Scholar 

  13. Dao, P. B., & Staszewski, W. J. (2013). Cointegration approach for temperature effect compensation in Lamb wave based damage detection. Smart Materials and Structures, 22(9), 095002.

    Article  Google Scholar 

  14. Dao, P. B. (2013). Cointegration method for temperature effect removal in damage detection based on Lamb waves. Diagnostyka, 14(3), 61–67.

    MathSciNet  Google Scholar 

  15. Dao, P. B., & Staszewski, W. J. (2014). Data normalisation for Lamb wave-based damage detection using cointegration: A case study with single- and multiple-temperature trends. Journal of Intelligent Material Systems and Structures, 25(7), 845–857.

    Article  Google Scholar 

  16. Dao, P. B., & Staszewski, W. J. (2014). Lamb wave based structural damage detection using cointegration and fractal signal processing. Mechanical Systems and Signal Processing, 49, 285–301.

    Article  Google Scholar 

  17. Dao, P. B., Staszewski, W. J., & Klepka, A. (2015). Stationarity-based approach for the selection of lag length in cointegration analysis used for structural damage detection. Computer-Aided Civil and Infrastructure Engineering, 32(2), 138–153.

    Article  Google Scholar 

  18. Zolna, K., Dao, P. B., Staszewski, W. J., & Barszcz, T. (2016). Towards homoscedastic nonlinear cointegration for structural health monitoring. Mechanical Systems and Signal Processing, 75, 94–108.

    Article  Google Scholar 

  19. Zolna, K., Dao, P. B., Staszewski, W. J., & Barszcz, T. (2015). Nonlinear cointegration approach for condition monitoring of wind turbines. Mathematical Problems in Engineering, Article ID 978156, 11.

    Google Scholar 

  20. Tsay, R. S. (2005). Analysis of financial time series (Vol. Wiley Series in Probability and statistics, 2nd ed., 576 pp.). New York: Wiley Interscience.

    Google Scholar 

  21. Zivot, E., & Wang, J. (2006). Modeling financial time series with S-PLUS (2nd ed.) Springer.

    Google Scholar 

  22. Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072.

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The work presented in this paper was supported by funding from the WELCOME research project no. 2010-3/2 sponsored by the Foundation for Polish Science (Innovative Economy, National Cohesion Programme, EU). The authors are grateful to the financial support from the Department of Robotics and Mechatronics at the AGH University of Science and Technology.

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Authors and Affiliations

  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Phong B. Dao, Wieslaw J. Staszewski & Tadeusz Uhl

Authors
  1. Phong B. Dao
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  2. Wieslaw J. Staszewski
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  3. Tadeusz Uhl
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Corresponding author

Correspondence to Phong B. Dao .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice, Poland

    Anna Timofiejczuk

  2. National School of Engineers of Sfax, Sfax, Tunisia

    Fakher Chaari

  3. Institute of Mining Engineering, Wrocław University of Technology, Wrocław, Poland

    Radoslaw Zimroz

  4. Institute of Mining Engineering, Wrocław University of Technology, Wrocław, Poland

    Walter Bartelmus

  5. National School of Engineers of Sfax, Sfax, Tunisia

    Mohamed Haddar

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Dao, P.B., Staszewski, W.J., Uhl, T. (2018). Operational Condition Monitoring of Wind Turbines Using Cointegration Method. In: Timofiejczuk, A., Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2016. Applied Condition Monitoring, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-61927-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-61927-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61926-2

  • Online ISBN: 978-3-319-61927-9

  • eBook Packages: EngineeringEngineering (R0)

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