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Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines

Author

Listed:
  • Konrad Zolna
  • Phong B. Dao
  • Wieslaw J. Staszewski
  • Tomasz Barszcz

Abstract

Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets—that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain—are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications.

Suggested Citation

  • Konrad Zolna & Phong B. Dao & Wieslaw J. Staszewski & Tomasz Barszcz, 2015. "Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, December.
    • Handle: RePEc:hin:jnlmpe:978156
    DOI: 10.1155/2015/978156
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    Cited by:

    1. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    2. Paweł Knes & Phong B. Dao, 2024. "Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach," Energies, MDPI, vol. 17(20), pages 1-21, October.

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