KIEE International Transactions on Electrophysics and Applications

The Korean Institute of Electrical Engineers (대한전기학회)
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A Study on the Pattern Recognition Rate of Partial Discharge in GIS using an Artificial Neural Network

  • Kang Yoon-Sik (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Lee Chang-Joon (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Kang Won-Jong (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Lee Hee-Cheol (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Park Jong-Wha (Power Testing & Technology Institute, LS Industrial Systems)
  • Published : 2005.04.01
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Abstract

This paper describes analysis and pattern recognition techniques for Partial Discharge(PD) signals in Gas Insulated Switchgears (GIS). Detection of PD signals is one of the most important factors in the predictive maintenance of GIS. One of the methods of detection is electro magnetic wave detection within the Ultra High Frequency (UHF) band (300MHz $\~$ 3GHz). In this paper, PD activity simulation is generated using three types of artificial defects, which were detected by a UHF PD sensor installed in the GIS. The detected PD signals were performed on three-dimensional phi-q-n analysis. Finally, parameters were calculated and an Artificial Neural Network (ANN) was applied for PD pattern recognition. As a result, it was possible to discriminate and classify the defects.

Keywords

References

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