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Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction

Author

Listed:
  • Zhixin Fu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Zihao Zhou

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Junpeng Zhu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Yue Yuan

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

Abstract

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6275-:d:1227939
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. José Raimundo Dantas Neto & José Soares Batista Lopes & Diego Antonio De Moura Fonsêca & Antonio Ronaldo Gomes Garcia & Jossana Maria de Souza Ferreira & Elmer Rolando Llanos Villarreal & Andrés Ortiz, 2024. "Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP," Energies, MDPI, vol. 17(5), pages 1-28, February.

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