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Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data

Author

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  • Mengnan Cao

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China)

  • Yingning Qiu

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China)

  • Yanhui Feng

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China)

  • Hao Wang

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China)

  • Dan Li

    (School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China)

Abstract

Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.

Suggested Citation

  • Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. "Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data," Energies, MDPI, vol. 9(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:847-:d:80973
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    References listed on IDEAS

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    1. Yingning Qiu & Wenxiu Zhang & Mengnan Cao & Yanhui Feng & David Infield, 2015. "An Electro-Thermal Analysis of a Variable-Speed Doubly-Fed Induction Generator in a Wind Turbine," Energies, MDPI, vol. 8(5), pages 1-17, April.
    2. Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
    3. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    4. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Lei Fu & Yanding Wei & Sheng Fang & Xiaojun Zhou & Junqiang Lou, 2017. "Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States," Energies, MDPI, vol. 10(10), pages 1-21, October.
    2. Linhai Zhu & Jinfu Liu & Yujia Ma & Weixing Zhou & Daren Yu, 2020. "A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines," Energies, MDPI, vol. 13(18), pages 1-19, September.
    3. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    4. Yingning Qiu & Lang Chen & Yanhui Feng & Yili Xu, 2017. "An Approach of Quantifying Gear Fatigue Life for Wind Turbine Gearboxes Using Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 10(8), pages 1-21, July.
    5. Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    6. Quan Zhou & Taotao Xiong & Mubin Wang & Chenmeng Xiang & Qingpeng Xu, 2017. "Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS," Energies, MDPI, vol. 10(7), pages 1-15, July.

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