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Estimating Health Condition of the Wind Turbine Drivetrain System

Author

Listed:
  • Peng Qian

    (Engineering Department, Lancaster University, Lancaster LA1 4YW, UK)

  • Xiandong Ma

    (Engineering Department, Lancaster University, Lancaster LA1 4YW, UK)

  • Dahai Zhang

    (Ocean College, Zhejiang University, Hangzhou 310058, China)

Abstract

Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM approach for wind turbines based on the online sequential extreme learning machine (OS-ELM) algorithm. A physical kinetic energy correction model is employed to normalize the temperature change to the value at the rated power output to eliminate the effect of variable speed operation of the turbines. The residual signal, obtained by comparing the predicted values and practical measurements, is processed by the physical correction model and then assessed with a Bonferroni interval method for fault diagnosis. Models have been validated using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains various types of temperature data of the gearbox. The results show that the proposed method can detect more efficiently both the long-term aging characteristics and the short-term faults of the gearbox.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1583-:d:114752
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    References listed on IDEAS

    as
    1. Peng Guo & David Infield, 2012. "Wind Turbine Tower Vibration Modeling and Monitoring by the Nonlinear State Estimation Technique (NSET)," Energies, MDPI, vol. 5(12), pages 1-15, December.
    2. Wenna Zhang & Xiandong Ma, 2016. "Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines," Energies, MDPI, vol. 9(4), pages 1-15, April.
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    Cited by:

    1. Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    2. Qian, Peng & Feng, Bo & Liu, Hao & Tian, Xiange & Si, Yulin & Zhang, Dahai, 2019. "Review on configuration and control methods of tidal current turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 125-139.
    3. 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.
    4. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    5. Aiman Abbas Mahar & Nayyar Hussain Mirjat & Bhawani S. Chowdhry & Laveet Kumar & Quynh T. Tran & Gaetano Zizzo, 2023. "Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique," Energies, MDPI, vol. 16(5), pages 1-16, March.
    6. Angel Gil & Miguel A. Sanz-Bobi & Miguel A. Rodríguez-López, 2018. "Behavior Anomaly Indicators Based on Reference Patterns—Application to the Gearbox and Electrical Generator of a Wind Turbine," Energies, MDPI, vol. 11(1), pages 1-15, January.

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