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Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis

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  • Zhu, Yongchao
  • Zhu, Caichao
  • Tan, Jianjun
  • Wang, Yili
  • Tao, Jianquan

Abstract

To reduce the operation & maintenance cost of the wind turbine and improve its reliability, we propose a novel combined method for real-time operational state prediction, based on the long short-term memory and fuzzy synthetic assessment. After analyzing and filtering the monitoring data of a 2-MW wind turbine gearbox (WTG), we propose a deep learning-based multi-index operational state assessment framework. Following this, the prediction dimensions of each assessment index are established based on the correlation analysis. Meanwhile, we have obtained each index's weight and membership degree after analyzing the prediction error based on Long Short-Term Memory (LSTM). Case studies are performed using three-month Supervisory Control and Data Acquisition (SCADA) data of a 2-MW WTG with fault information. The results demonstrate that the difference between normal and fault state is more prominent when the prediction dimensions with lower correlation are selected. The degree of fault reflected by different assessment indexes is distinguished even under the same state. Then, through reviewing the alarm history of the condition monitoring system, we find that the proposed method can be used to detect the potential failures of the WTG.

Suggested Citation

  • Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
  • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:1167-1176
    DOI: 10.1016/j.renene.2021.09.070
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    1. Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    2. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
    3. Orlando, Andrea & Pagnini, Luisa & Repetto, Maria Pia, 2021. "Structural response and fatigue assessment of a small vertical axis wind turbine under stationary and non-stationary excitation," Renewable Energy, Elsevier, vol. 170(C), pages 251-266.
    4. 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.
    5. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
    6. Qian, Peng & Zhang, Dahai & Tian, Xiange & Si, Yulin & Li, Liangbi, 2019. "A novel wind turbine condition monitoring method based on cloud computing," Renewable Energy, Elsevier, vol. 135(C), pages 390-398.
    7. Yao Li & Caichao Zhu, 2018. "Reliability Analysis of Wind Turbines," Chapters, in: Kenneth Eloghene Okedu (ed.), Stability Control and Reliable Performance of Wind Turbines, IntechOpen.
    8. Jia, Xiaodong & Jin, Chao & Buzza, Matt & Wang, Wei & Lee, Jay, 2016. "Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves," Renewable Energy, Elsevier, vol. 99(C), pages 1191-1201.
    9. Guezuraga, Begoña & Zauner, Rudolf & Pölz, Werner, 2012. "Life cycle assessment of two different 2 MW class wind turbines," Renewable Energy, Elsevier, vol. 37(1), pages 37-44.
    10. Li, Xuan & Zhang, Wei, 2020. "Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures," Renewable Energy, Elsevier, vol. 147(P1), pages 764-775.
    11. Alvarez, Eduardo J. & Ribaric, Adrijan P., 2018. "An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA," Renewable Energy, Elsevier, vol. 115(C), pages 391-399.
    12. Ruiz de la Hermosa González-Carrato, Raúl, 2017. "Sound and vibration-based pattern recognition for wind turbines driving mechanisms," Renewable Energy, Elsevier, vol. 109(C), pages 262-274.
    13. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    14. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.
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    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.
    3. 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.

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