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Diagnosis of wind turbine faults with transfer learning algorithms

Author

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  • Chen, Wanqiu
  • Qiu, Yingning
  • Feng, Yanhui
  • Li, Ye
  • Kusiak, Andrew

Abstract

A framework of using transfer learning algorithms, Inception V3 and TrAdaBoost, for fault diagnosis of two wind turbine faults is presented and verified. Two failure modes, blade icing accretion and gear cog belt fracture, are analyzed using SCADA data. A new index named ‘Comprehensive Index’ is defined to evaluate performance of different algorithms. Traditional machine learning algorithms do not perform well for data sets that are unbalanced and follow different distributions. The former causes bias in classification and the latter leads to poor adaptability of algorithms. A novel transfer learning algorithm studied in this paper, TrAdaBoost, has been proved to have superior performance on dealing with data imbalance and different distributions. A new approach to calibrate data labels using transfer learning algorithms is also proposed, which provides important insights into unsupervised learning for wind turbine fault diagnosis.

Suggested Citation

  • Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:2053-2067
    DOI: 10.1016/j.renene.2020.10.121
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    References listed on IDEAS

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    Cited by:

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    5. Rami Al-Hajj & Ali Assi & Bilel Neji & Raymond Ghandour & Zaher Al Barakeh, 2023. "Transfer Learning for Renewable Energy Systems: A Survey," Sustainability, MDPI, vol. 15(11), pages 1-28, June.
    6. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Song, Chaosheng & Chen, Dingliang & Zheng, Jie, 2022. "Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion," Renewable Energy, Elsevier, vol. 200(C), pages 1023-1036.
    7. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    8. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    9. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    10. Bai, Xinjian & Tao, Tao & Gao, Linyue & Tao, Cheng & Liu, Yongqian, 2023. "Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing," Renewable Energy, Elsevier, vol. 211(C), pages 412-419.
    11. Silvio Simani & Saverio Farsoni & Paolo Castaldi, 2023. "RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-22, April.
    12. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
    13. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
    14. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    15. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    16. Gürdal Ertek & Lakshmi Kailas, 2021. "Analyzing a Decade of Wind Turbine Accident News with Topic Modeling," Sustainability, MDPI, vol. 13(22), pages 1-34, November.
    17. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
    18. Xiao Wang & Zheng Zheng & Guoqian Jiang & Qun He & Ping Xie, 2022. "Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network," Energies, MDPI, vol. 15(8), pages 1-19, April.

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