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A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network

Author

Listed:
  • Xiange Tian

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yongjian Jiang

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

  • Chen Liang

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

  • Cong Liu

    (Southwest Technology and Engineering Research Institute, Chongqing 400039, China)

  • You Ying

    (Zhejiang Windey Co., Ltd., Hangzhou 310012, China)

  • Hua Wang

    (Huaneng Clean Energy Research Institute, Beijing 102209, China)

  • Dahai Zhang

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

  • Peng Qian

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

Abstract

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6717-:d:914622
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    4. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    5. 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.
    6. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
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    Cited by:

    1. Zhixin Fu & Zihao Zhou & Junpeng Zhu & Yue Yuan, 2023. "Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
    2. Sun, Shilin & Li, Qi & Hu, Wenyang & Liang, Zhongchao & Wang, Tianyang & Chu, Fulei, 2023. "Wind turbine blade breakage detection based on environment-adapted contrastive learning," Renewable Energy, Elsevier, vol. 219(P2).

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