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A novel wind turbine condition monitoring method based on cloud computing

Author

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  • Qian, Peng
  • Zhang, Dahai
  • Tian, Xiange
  • Si, Yulin
  • Li, Liangbi

Abstract

With the development of condition monitoring technology, the data collected by sensors are voluminous and much faster than before. The cloud computing technology is a good solution for big data processing, it is therefore very suitable to be applied in the condition monitoring of the wind turbine, especially for data-driven model-based condition monitoring methods. In order to solve this problem, a novel wind turbine condition monitoring method based on cloud computing is proposed in this paper. A data-driven model-based condition monitoring (CM) method by using hierarchical extreme learning machine (H-ELM) algorithm is adopted to achieve fault detection of the gearbox in the wind turbine, which has better performance than traditional ELM method. Then, compressed sensing (CS) method is applied to compress the first hidden layer output that will be uploaded to the cloud for further calculation. The proposed method is not only able to detect the faults effectively, but also considering data upload quantity reduction and data security. The case study validates the effectiveness of the proposed method. Consequently, it is effective and can also enhance economic benefit and operating efficiency of the wind farm.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:390-398
    DOI: 10.1016/j.renene.2018.12.045
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    References listed on IDEAS

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    1. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    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. 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.
    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. Shivaji Karad & Ritula Thakur, 2021. "Efficient monitoring and control of wind energy conversion systems using Internet of things (IoT): a comprehensive review," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 14197-14214, October.
    4. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    5. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    6. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
    7. Zhaoyan Zhang & Shaoke Wang & Peiguang Wang & Ping Jiang & Hang Zhou, 2022. "Research on Fault Early Warning of Wind Turbine Based on IPSO-DBN," Energies, MDPI, vol. 15(23), pages 1-18, November.
    8. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    9. 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.
    10. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
    11. Xiange Tian & Yongjian Jiang & Chen Liang & Cong Liu & You Ying & Hua Wang & Dahai Zhang & Peng Qian, 2022. "A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network," Energies, MDPI, vol. 15(18), pages 1-15, September.
    12. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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