• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 추계학술대회 논문집 Vol.17
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• pp.679-682
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• 2004

GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized accordingly. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected signals with the application of neural network algorithm.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.2
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• pp.1207-1210
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• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 제6회 학술대회 논문집 일렉트렛트 및 응용기술연구회
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• pp.1-4
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• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this anslysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 추계학술대회 논문집 Vol.16
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• pp.575-578
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• 2003

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 하계학술대회 논문집 Vol.4 No.2
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• pp.1196-1199
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• 2003

Recently, the development of diagnosis technique with high confidence is important on power equipment, for this reason is use for measurement and analysis of PD with prior appearance of insulation breakdown. In this paper, we presents the analysis of trouble signal to use ${\phi}-Q$ method which display 2D and 3D Equipment of simulation has made independently DS for 170kV GIS of one phase with same on field. The detected signal through the sensor of Induction and Acoustic Emission is classified to use characteristic neural network algorithm and then it is analysis.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 제5회 학술대회 논문집 일렉트렛트 및 응용기술연구회
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• pp.29-32
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• 2003

Recently, the development of diagnosis technique with high confidence is important on power equipment, for this reason is to use for measurement and analysis of PD with prior appearance of insulation breakdown In this paper, we presents the analysis of trouble signal to use both method of general analysis of $\Phi$-Q-N in PD and statistical parameter by this interpretation Equipment of simulation has made independently DS for 170kV GIS of one phase with same on field. The detected signal through the sensor of Induction and Acoustic Emission is classified which used to characteristic neural network algorithm and then it is analysis.

• 전기학회논문지
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• 제57권1호
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• pp.46-51
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• 2008

Extra-high voltage(EHV) disconnecting switch(DS) consists of the electrical contacts and mechanical parts which actuate the contacts. When the short-circuit condition occurs, a large amount of current flows through the electrical contact in disconnecting switches and this causes considerable temperature rise due to Joule heating. If the temperature rise is higher than the melting point of contact material, the DS contact becomes melting and cannot be usable anymore. For this reason, the analysis for capability of carrying short-circuit current in DS contacts must be performed at a design stage. Here, we proposed a numerical technique for evaluating the capability of carrying short-circuit current at electrical contacts in EHV DS. In this numerical approach, the mechanical and thermal analyses were simulated to check the capability of carrying short-circuit current. First, the applied pressure at contact parts was analyzed considering the mechanical properties, and then contact resistance was calculated by an empirical equation. Finally, thermal analysis was performed with resistance variation at electrical contacts. To verify these numerical results, the distributions of temperature in DS were experimentally measured and compared with each other. The results from experiments were agreed well with those from the proposed numerical simulations.

• 한국전기전자재료학회논문지
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• 제16권12S호
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• pp.1310-1315
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• 2003

GIS(Gas Insulated Switchgear) is power equipment with excellent dielectric strength and is economy merit in high confidence and stability. But, because of structural characteristics, this is difficult to monitor externally and provide much loss in the event of an accident. Recently, because equipment of GIS is occurring problem of confidence used for a long time, development of diagnosis technique have been importantly recognized. therefore, measurement and analysis of PD much has been generally used for equipment of GIS. But, in case of measurement of PD at field, real trouble signals is difficult to classify noise. Accordingly, a variety of trouble conditions for DS are simulated, and detected signals are analyzed by the application of electrical and mechanical methods. For this analysis, detected signals are accumulated according to phase-magnitude with the application of induction sensor, and then we analyzed the characteristics. For the simulation experiment, we make DS for 170KV GIS and analyze the characteristics of detected signals with the application of neural network algorithm. last, we have measured DS for GIS at field, and then have analyzed detected signals.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 C
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• pp.1759-1761
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• 2003

Recently, the development of diagnosis technique with high confidence is important on power equipment, for this reason is use for measurement and analysis of PD with prior appearance of insulation breakdown In this paper, we presents the analysis of trouble signal to use ${\Phi}$-Q method which display 2D and 3D Equipment of simulation has made independently DS for 170kV GIS of one phase with same on field. The detected signal through the sensor of Induction and Acoustic Emission is classified to use characteristic neural network algorithm and then it is analysis.