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부분방전 진단을 위한 초음파 계측 및 딥러닝기법의 응용
채민우(Min-Woo Chae),강봉수(Bong-Soo Kang) 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.11
This paper presents an innovative diagnosis method for partial discharge which may damage the components of a high-voltage electrical equipment and cause power outbreak. Instead of using the conventional direct-attachment method, it is convenient to measure the arc noise produced by partial discharge from a safe distance. Then, the source signal is converted to a phase-resolved graph learned through the convolutional neural network. The proposed deep-learning scheme, after learning different phase-resolved graphs obtained from three types of partial discharges and environmental noises measured in the actual laboratory, exhibited a prediction accuracy of more than 94 % for the test samples. Therefore, if this technique is embedded on an ultrasonic camera, then a non-specialist like a gas inspector can easily diagnose the high-voltage facilities distributed over a broad area.