• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 C
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• pp.1800-1802
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• 2003

This paper describes a study on gas dissolved distribution in oil for simulated transformer thermal faults. Experimental chamber was setup for simulation of transformer thermal faults or discharge in oil with or without insulation paper. The experimental results showed that dissolved gases in oil excluding the paper did not evolved upto $150^{\circ}C$. Hereafter the planned gas dissolved analysis will be continuously carried out for transformer fault conditions with or without insulation paper related to water absorption, arc and partial discharge.

• 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.

• 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.

• 조명전기설비학회논문지
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• 제21권9호
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• pp.87-94
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• 2007

This paper presents new criteria for DGA(Dissolved Gases Analysis) using CDF(Cumulative Distribution Function) obtained from the data from the diagnosis of transformers operated in KEPCO over a period of 16 years. Because of differences in operating environments, construction type, oil volume, and other factors, the interpretative criteria of DGA at KEPCO differs from other standards such as IEC-60599, or Rogers and Doernenburg. To suggest the most appropriate criteria, the DGA data from transformers under normal conditions as well as from developing fault transformers were collected. Using these data, this study suggests the limitative gas level of transformers under normal operating conditions and verifies the suitability of the criteria. Because the application of this new criterion to transformers at KEPCO increases the detectable ratio of incipient faults and reduces unnecessary follow-up sampling and analysis, the new criteria yields a more reliable prediction of transformer condition.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 학술대회 논문집
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• pp.87-90
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• 2004

This paper describes development of power transformer maintenance system using intelligent dissolved gases in oil analysis. The used gases are acetylene(C2H2), hydrogen(H2), ethylene(C2H4), methane(CH4), ethane(C2H6), carbon monoxide(CO) and carbon dioxide(CO2). The rule and neural network based gas analysis methods are used for artificial intelligent diagnosis. It is indicated that this program is efficient for diagnosis of oil immersed transformers diagnosis from application of gas analysis data of serviced transformer which has local overheating

• 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 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.

• 전기학회논문지P
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• 제59권2호
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• pp.212-217
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• 2010

When paper which was applied as insulation in oil-filled transformer was aged by thermal, its electrical, mechanical and chemical characteristics were changed and deteriorated. Therefore operating temperature was more higher, damage of paper was more quicker. Insulating paper which was generally made with cellulose was degraded, polymer of long length chain was decomposed as a monomer and CO, $CO_2$ gas and/or by-product such as furfural was produced from paper at the same time. In according with detection these gas and furfural by dissolved gas analysis(DGA) and high performance liquid chromatography(HPLC), we have investigated effects of CO, $CO_2$ gas and furfural on insulation of paper. Also we have analyzed for correlation between furfural and CO, $CO_2$ gas using linear regression method that was known as useful, credible statistical analysis.

• 상하수도학회지
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• 제30권6호
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• pp.635-643
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• 2016

Gas hydrate (GH)-based desalination process have a potential as a novel unit desalination process. GHs are nonstoichiometric crystalline inclusion compounds formed at low temperature and a high pressure condition by water and a number of guest gas molecules. After formation, pure GHs are separated from the remaining concentrated seawater and they are dissociated into guest gas and pure water in a low temperature and a high pressure condition. The condition of GH formation is different depending on the type of guest gas. This is the reason why the guest gas is a key to success of GH desalination process. The salt rejection of GH based desalination process appeared 60.5-93%, post treatment process is needed to finally meet the product water quality. This study adopted reverse osmosis (RO) as a post treatment. However, the test about gas rejection by RO process have to be performed because the guest gas will be dissolved in a GH product (RO feed). In this research, removal potential of dissolved gas by RO process is performed using lab-scale RO system and GC/MS analysis. The relation between RO membrane characteristics and gas removal rate were analyzed based on the GC/MS measurement.