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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)
Publication Information
KIEE International Transactions on Electrophysics and Applications / v.5C, no.2, 2005 , pp. 63-66 More about this Journal
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
PD; UHF; GIS diagnostic system; pattern recognition;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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