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A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation

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  • Ren, He
  • Liu, Wenyi
  • Shan, Mengchen
  • Wang, Xin
  • Wang, Zhengfeng

Abstract

Aimed at the problem that the complicated working condition of wind turbine and the lack of sufficient target samples, which makes it difficult to conduct effective health condition monitoring (HCM), a novel method based on composite variational mode entropy (CVME) and weighted distribution adaptation (WDA) is proposed in this paper. A series of mode components are first obtained by performing variational mode decomposition (VMD) on the signals under various working conditions. The mode components are analyzed on multi-scale, and then the fuzzy entropy is extracted at different scales. The extracted CVME is input as a feature vector into WDA. The WDA method can effectively reduce the discrepancy of data distribution between the source and target domains by adjusting the weight of the marginal distribution and the conditional distribution, and solve the problem of class imbalance in domains by a weight matrix. The transferability evaluation is used to select the feature sets under auxiliary working conditions with high similarity to the target feature set as the source samples. Finally, the source and target samples are input into the classifier for training and testing. Compared with traditional fault diagnosis methods, experiment shows that the proposed method has higher accuracy in wind turbine fault diagnosis under variable working conditions.

Suggested Citation

  • Ren, He & Liu, Wenyi & Shan, Mengchen & Wang, Xin & Wang, Zhengfeng, 2021. "A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation," Renewable Energy, Elsevier, vol. 168(C), pages 972-980.
    • Handle: RePEc:eee:renene:v:168:y:2021:i:c:p:972-980
    DOI: 10.1016/j.renene.2020.12.111
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    1. Naik, Jyotirmayee & Dash, Pradipta Kishore & Dhar, Snehamoy, 2019. "A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression," Renewable Energy, Elsevier, vol. 136(C), pages 701-731.
    2. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    3. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    4. Hao Duan & Ming Lu & Yongteng Sun & Jinyu Wang & Cheng Wang & Zuguo Chen, 2020. "Fault Diagnosis of PMSG Wind Power Generation System Based on LMD and MSE," Complexity, Hindawi, vol. 2020, pages 1-11, July.
    5. Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
    6. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    7. Hu, Yaogang & Li, Hui & Shi, Pingping & Chai, Zhaosen & Wang, Kun & Xie, Xiangjie & Chen, Zhe, 2018. "A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process," Renewable Energy, Elsevier, vol. 127(C), pages 452-460.
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