• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• Journal of the Korean Vacuum Society
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• 제18권1호
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• pp.66-72
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• 2009

The detection methods are required to monitor and diagnose the abnormality on the insulation condition inside a gas-insulated switchgear (GIS). Due to a good sensitivity to the products decomposed by partial discharges (PDs) in $SF_6$ gas, the development of a SWNT gas sensor is actively in progress. However, a few numerical studies on the diffusion mechanism of the $SF_6$ decomposition products by PD have been reported. In this study, we modeled $SF_6$ decomposition process in a chamber by calculating temperature, pressure and concentration of the decomposition products by using a commercial CFD program in conjunction with experimental data. It was assumed that the mass production rate and the generation temperature of the decomposition products were $5.04{\times}10^{-10}$ [g/s] and over 773 K respectively. To calculate the concentration equation, the Schmidt number was specified to get the diffusion coefficient functioned by viscosity and density of $SF_6$ gas instead rather than setting it directly. The results showed that the drive potential is governed mainly by the gradient of the decomposition concentration. A lower concentration of the decomposition products was observed as the sensors were placed more away from the discharge region. Also, the concentration increased by increasing the discharge time. By installing multiple sensors the location of PD is expected to be identified by monitoring the response time of the sensors, and the information should be very useful for the diagnosis and maintenance of GIS.