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합성곱 신경망 기반의 위상 불변에 강건한 GIS PD 고장 진단 모델 개발
박종민(Jongmin Park),김선의(Sun Uwe Kim),김수호(Sooho Kim),정진교(Jin-gyo Jung),윤병동(Byeng D.Youn) 대한기계학회 2018 대한기계학회 춘추학술대회 Vol.2018 No.12
Gas Insulated Switchgear(GIS) is electrical equipment for stable transformation or transmission. Since GIS serves to transmit or disconnect high voltage currents, it is very important to maintain stable internal isolation condition. Previous researches have been conducted to detect Partial Discharge(PD) by Ultra High Frequency(UHF)sensors, focusing on partial discharge among the causes that deteriorate internal isolation condition. In this study, PD diagnostic method was developed using Phase Resolved Pulse Sequence(PRPS) image, which is an image of the UHF sensor signal of GIS used in the actual field. For actual data, there is a phase shift phenomenon of PRPS images due to measurement or synchronization errors, which makes it difficult to diagnose them. To solve this problem, a Convolutional Neural Network(CNN) based Deep learning architecture robust to phase shift is proposed and it showed high diagnosis accuracy compared to previous algorithm.