• 대한전기학회논문지:전력기술부문A
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• 제49권1호
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• pp.31-36
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• 2000

To enhance the fault diagnosis ability for the dissolved gas analysis(DGA) of the power transformer, this paper proposes a learning vector quantization(LVQ) for the incipient fault recognition. LVQ is suitable expecially for pattern recognition such as fault diagnosis of power transformer using DGA because it improves the performance of Kohonen neural network by placing emphasis on the classification around the decision boundary. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Korea Electrical Power Corporation.

• 한국재료학회지
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• 제4권3호
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• pp.312-318
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• 1994

이 논문은 절연유를 정제하여 $SF_6$ 가스를 용해시켰을 때 절연유의 수십 MHz대의 고주파 절연파괴 전압과 유전특성에 관해서 연구한 것이다. 1)절연유에서의 고주파 전류는 전압의 1/2승에 거의 비례하여 증가한다. 2)전원주파수가 증가함에 따라 고주파 절연파괴는 주파수의 제곱에 역비례하여 감소하는 현상을 나타내고 AC절연파괴의 전압때보다 약 35%정도가 감소한다. 3) 용해된 기체의 압력을 증가시킴에 따라 고주파 절연파괴 전압은 원만하게 증하한다. 4)전원주파수의 증가에 따른 유전정접(tan $\delta$)은 거의 지수 함수적으로 증가하고 유전률($\varepsilon$)은 [0.6% MHZ]의 기울기로 삼소하는 경향을 나타낸다. 5) $SF_6$ 가스를 용해시켰을때 절연파괴 특성은 Air 또는 Ar을 용해시켰을 때 보다 약 25% 정도 향상되는 특성을 나타낸다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.859-861
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• 1996

This paper presents the novel fault diagnostic expert system based on dissolved gas analysis(DGA) techniques in power transformer. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set concept. The uncertainty of rules are handled by fuzzy measures. Trend analysis through the monthly increment of key gas and DGA analysis are combined by the Dempster-Shafer theory, and the state of transformer and confidence factor are yielded by using this combined analysis. To verify the effectiveness of the proposed diagnosis technique, the expert system has been tested by using KEPCO's transformer gas records.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.207-210
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• 2004

In oil-filled equipment such as transformers, partial discharge or local overheating will make insulating material(oil, kraft paper, proclain and wood) be stressed and generate many sort of gases($CO,\;CO_2,\;H_2,\;C_2H_4$) which are dissolved in transformer oil. The ratio of this gas can make diagnostic tecchniques of the lifetime of transfomer so, it is important to monitoring $H_2$ gas continuously. This paper developes a system of detecting about $H_2$ gas by using $H_2$ gas sensor, and we describe operation and performance of this system

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 C
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• pp.1842-1844
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• 2004

In oil-filled equipment such as transformers, partial discharge or local overheating will make insulating material(oil, kraft paper, proclain and wood) be stressed and generate many sort of gases(CO, $CO_2,\;H_2,\;C_2H_4$) which are dissolved in transformer oil. The ratio of this gas can make diagnostic tecchniques of the lifetime of transfomer so, it is important to monitoring $H_2$ gas continuously. This paper developes a system of detecting about $H_2$ gas by using $H_2$ gas sensor, and we describe operation and performance of this system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.1463-1464
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• 2015

This paper describes for the diagnosis insulating oil for transformers integrity assessment. The high-capacity oil transformer has several insulators are entered for the purpose of insulation, such as insulating oil, insulating paper and press board. Irradiated with a gas component dissolved in the insulating oil can analyze the status of the transformer, and can prevent a sudden accident in advance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.952-954
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• 1998

This paper presents an artificial neural network approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. The proposed algorithm is used to detect faults with or without cellulose involved. Several neural network topologies have been considered. Good diagnosis accuracy is obtained with the proposed approach.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2058-2064
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• 2014

In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.864-866
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• 1997

This paper presents fault recognition and interpretation in power transformers using dissolved gas analysis embedded Kohonen feature mapping. The imprecision of gas ratio analysis in dissolved gas analysis are managed by mapping in accordance with learning of Kohonen neural network. To verify the effectiveness of the proposed system, it has been tested by the historical gas records to power transformers of Korea Electric Power Corporation. More appropriate fault types can support the maintenance personnels to increase the disgnostic performance for fault of power transformers.

• 대한전기학회논문지
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• 제41권2호
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• pp.178-184
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• 1992

This study treats the improvement of the dielectric strength of polymer by eliminating the air in it and dissolving electronegative gases. As experimental material, unsaturated polyester resin was used and the specimen was made by dissolving NS12T. SFS16T abd CCIS12TFS12T gases which have strong electron affinity. And also the electrical properties (tree growth and breakdown characteristics) of them were tested and discussed. The results are as follows. When the specimen dissolved with electronegative gas compared with one with air` 1) The tree breakdown voltage of the former is higher than that of the latter. 2) The tree growth of the former is slower than that of the latter. 3) The temperature dependence of the former is smaller than that of the latter. 4) The breakdown voltage of the specimen dissolved with electronegative gas is much higher than that dissolved with air.