Journal of Information Processing Systems

  • 제16권4호
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  • Pages.870-881
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  • 2020
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Research on Fault Diagnosis of Wind Power Generator Blade Based on SC-SMOTE and kNN

  • Peng, Cheng ;
  • Chen, Qing (School of Computer Science, Hunan University of Technology) ;
  • Zhang, Longxin (School of Computer Science, Hunan University of Technology) ;
  • Wan, Lanjun (School of Computer Science, Hunan University of Technology) ;
  • Yuan, Xinpan (School of Computer Science, Hunan University of Technology)
  • 투고 : 2019.05.31
  • 심사 : 2019.12.21
  • 발행 : 2020.08.31
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초록

Because SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repeating instances by interpolating adjacent minority samples. In order to overcome the shortcomings of these methods which does not consider boundary conditions in balancing data, an improved over-sampling balancing algorithm SC-SMOTE (safe circle synthetic minority oversampling technology) is proposed to optimize data sets. Then, for the balanced data sets, a fault diagnosis method based on improved k-nearest neighbors (kNN) classification for wind turbine blade icing is adopted. Compared with the SMOTE algorithm, the experimental results show that the method is effective in the diagnosis of fan blade icing fault and improves the accuracy of diagnosis.

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