• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.1
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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.

• Korean Journal of Materials Research
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• v.4 no.3
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• pp.312-318
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• 1994

In this paper the breakdown and dielectric characteristics of purified transformer oil dissolved with $SF_6$ Gas are investigated with a few decade MHz frequency voltage. The results are as follows. 1) High frequency current is a approximately proportional to the square root of high frequency voltage in purified transformer oil. 2) As frequency increase breakdown voltage decrease inversely proportional to the square root of frequency and the high frequency breakdown voltage is lower about 35 percentage than that of AC 3) The breakdown voltage of high frequency has a little increase with the pressure increase of dissolved $SF_6$, Air and Ar Gas. 4) As voltage freguency increases the value of the dielectric loss tangent has increased almost exponentially and the dielectric constant ($\varepsilon$) has tended to decrease with a slope[0.6% MHz]. 5) When dissolved with $SF_6$ Gas, oil electrical characteristics has more increased about 25% than in Air or Ar gas with high voltage frequency.

• Proceedings of the KIEE Conference
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• 1996.07b
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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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

• Proceedings of the KIEE Conference
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• 2004.07c
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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.

• Proceedings of the KIEE Conference
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• 2015.07a
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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.

• Proceedings of the KIEE Conference
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• 1998.07c
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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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• v.9 no.6
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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.

• Proceedings of the KIEE Conference
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• 1997.07c
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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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.2
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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.