KIEE International Transactions on Electrophysics and Applications

The Korean Institute of Electrical Engineers (대한전기학회)
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Comparison of Classification Rate Between BP and ANFIS with FCM Clustering Method on Off-line PD Model of Stator Coil

  • Park Seong-Hee (School of Electrical and Computer Engineering, Chungbuk National University) ;
  • Lim Kee-Joe (School of Electrical and Computer Engineering, Chungbuk National University) ;
  • Kang Seong-Hwa (Dept. of fire prevention Engineering, Chungcheong University) ;
  • Seo Jeong-Min (School of Electrical and Computer Engineering, Chungbuk National University) ;
  • Kim Young-Geun (LS industrial system)
  • Published : 2005.06.01
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Abstract

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

Keywords

References

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