• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.3
• /
• pp.349-356
• /
• 2015

Remote monitoring partial discharge sensor, equipping with hybrid filter combining optical and electrical noise reduction capabilities and with signal integrating function to calculate total arc energy, was investigated. Hybrid filter showed insensitivity to fluorescent and incandescent lamps under simulated distribution panel condition. Signal integrating function showed selective detection capability corresponding to different arc energy levels, while convention arc sensor had difficulty to discriminate arc energy level due to bursty arc waveform and peak level detection characteristics. The sensor showed possibility for application to remote monitoring partial discharge sensor, since it detected arc energy level corresponding to normal open and close discharge in low voltage 100A MCCB at 2m distance.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.10
• /
• pp.243-247
• /
• 2014

In this paper, we design two types of the sensor module to measure the partial discharge causing failures and electric fires for power incoming/distributer. And evaluate the performance thereof. Analyze the correlation of the phase current and the partial discharge and overheating. Proposes to apply the stability index by utilizing the analyzed information and use in order to prevent the failure or electrical fire of the power incoming/distributer.

• Proceedings of the KIEE Conference
• /
• 2003.07a
• /
• pp.354-356
• /
• 2003

본 논문에서 제시한 전력감시 시스템은 원주권광역상수도에 적용된 개방형 SCADA 시스템으로 무인화 사업장에 널리 사용되는 설비로서 중앙제어실에서 CRT화면을 통하여 원거리에 위치한 현장설비를 감시할 수 있으며 필요시 제어 명령을 보내서 해당기기를 제어하도록 구성되어 있다. 전기설비를 감시하는 모든 계전기는 디지탈로 구성되어 있어 전압, 전류값을 추적하고 판독하도록 되어 있다. 특히 각 판넬 내부에 설치된 폐쇄배전반 절연열화 진단설비는 전력설비 열화 시 발생하는 부분방전 검출을 위해 30MHz대역의 협대역으로 선정되어 있으며, 1초 동안 펄스 크기와 펄스 수량을 측정하여 열화진행과정을 실시간으로 근무자에게 통보하고, 또한 화재감시센서를 이용하여 화재발생 시 배전반 상부에 설치된 개별형 자동소화기가 동작하여 사고 파급을 막도록 구성하였다.

• Journal of Platform Technology
• /
• v.8 no.3
• /
• pp.10-21
• /
• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.