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A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines

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  • Phong B. Dao

    (Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3236-:d:566961
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    1. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    2. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
    3. Yolanda Vidal & Francesc Pozo & Christian Tutivén, 2018. "Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data," Energies, MDPI, vol. 11(11), pages 1-18, November.
    4. Diego Comin & Danial Lashkari & Martí Mestieri, 2021. "Structural Change With Long‐Run Income and Price Effects," Econometrica, Econometric Society, vol. 89(1), pages 311-374, January.
    5. Mehdi Baghli & Claude Delpha & Demba Diallo & Abdelhamid Hallouche & David Mba & Tianzhen Wang, 2019. "Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis," Energies, MDPI, vol. 12(7), pages 1-20, April.
    6. Paul Turner, 2010. "Power properties of the CUSUM and CUSUMSQ tests for parameter instability," Applied Economics Letters, Taylor & Francis Journals, vol. 17(11), pages 1049-1053.
    7. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    8. Logan T Lewis & Ryan Monarch & Michael Sposi & Jing Zhang, 2022. "Structural Change and Global Trade," Journal of the European Economic Association, European Economic Association, vol. 20(1), pages 476-512.
    9. Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
    10. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-1065, September.
    11. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    12. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    13. Kleiber, Christian, 2016. "Structural Change in (Economic) Time Series," Working papers 2016/06, Faculty of Business and Economics - University of Basel.
    14. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    15. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    16. 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, Elsevier, vol. 13(1), pages 1-39, January.
    17. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    18. Cheng-Few Lee & John C. Lee (ed.), 2015. "Handbook of Financial Econometrics and Statistics," Springer Books, Springer, edition 127, number 978-1-4614-7750-1, September.
    19. Kramer, Walter & Ploberger, Werner & Alt, Raimund, 1988. "Testing for Structural Change in Dynamic Models," Econometrica, Econometric Society, vol. 56(6), pages 1355-1369, November.
    20. Achim Zeileis & Friedrich Leisch & Christian Kleiber & Kurt Hornik, 2005. "Monitoring structural change in dynamic econometric models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 99-121, January.
    21. Peng Qian & Xiandong Ma & Dahai Zhang, 2017. "Estimating Health Condition of the Wind Turbine Drivetrain System," Energies, MDPI, vol. 10(10), pages 1-19, October.
    22. 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.
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    Cited by:

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