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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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    Cited by:

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    3. Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    4. Dao, Phong B. & Barszcz, Tomasz & Staszewski, Wieslaw J., 2024. "Anomaly detection of wind turbines based on stationarity analysis of SCADA data," Renewable Energy, Elsevier, vol. 232(C).
    5. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    6. Dao, Phong B., 2022. "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data," Renewable Energy, Elsevier, vol. 185(C), pages 641-654.
    7. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. Sarah Barber & Unai Izagirre & Oscar Serradilla & Jon Olaizola & Ekhi Zugasti & Jose Ignacio Aizpurua & Ali Eftekhari Milani & Frank Sehnke & Yoshiaki Sakagami & Charles Henderson, 2023. "Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation," Energies, MDPI, vol. 16(8), pages 1-23, April.
    9. Erginbay Ugurlu & Irena Jindřichovská, 2022. "Effect of COVID-19 on International Trade among the Visegrad Countries," JRFM, MDPI, vol. 15(2), pages 1-20, January.
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