• KIEE International Transactions on Electrophysics and Applications
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• 제3C권4호
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• pp.130-135
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• 2003

This study describes neural network based dissolved gas analysis using composition patterns of gas concentrations for transformer fault diagnosis. DGA samples were gathered from related literatures and classified into six types of faults and then a neural network was trained using the DGA samples. Diagnosis tests were performed by the trained neural network with DGA samples of serviced transformers, fault causes of which were identified by actual inspection. Diagnosis results by the neural network were in good agreement with actual faults.

• 한국정보통신학회논문지
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• 제17권10호
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• pp.2431-2438
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• 2013

본 논문에서는 휴대용 유중가스 분석기에 대해 연구 및 개발 하였다. 제안된 휴대용 유중가스 분석기는 전력기기로부터 절연유 내의 소량의 가스를 추출하여 비정상적인 열적 정보를 얻어 분석하는데 유용하게 쓰인다. 분석된 정보로부터 전력기기의 안정성을 평가할 수 있다. 본 논문에서는 기존의 고가의 시설기반의 고정형 유중 가스 분석기를 사용함으로써 발생되는 직간접적인 문제점을 해결하기 위해서 광음향 분광법 기반의 소형화된 휴대용 유중가스 분석기를 제안하고 실험적으로 검토하였다. 제안된 휴대용 유중 가스 분석기는 사용하기 편하고 실시간으로 전력기기의 열적 정보를 분석할 수 있다. 제안된 휴대용 유중가스 분석기가 현재의 전력기기 유중가스 분석에 사용된다면, 전력기기의 유중가스 분석주기를 줄임으로써 안정성을 향상 시키고 열적 이상 정보에 대하여 긴밀하게 대응할 수 있다.

• 전기학회논문지
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• 제59권10호
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• pp.1869-1873
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• 2010

For reliable operation of oil-filled electrical equipment, monitoring and maintenance of insulating oil is essential. Dissolved gas analysis(DGA) is widely used for monitoring faults in high voltage electrical equipment in service. Therefore, oil analysis should be monitored regularly during its service life. KEPCO has investigated thousands of dissolved gas analysis data since 1985, and conducted studies on the relationship of gas in oil analysis and internal inspection results of transformer. As the results, KEPCO revised criteria for transformer diagnosis and has applied it since 2008. Almost of 100 cases of internal inspection results since 2001 have been investigated. This paper presents the correlation of the fault-identifying gases with faults found in actual transformers and how should we approach to internal inspection of transformer by dissolved gas analysis.

• 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 2007년도 제38회 하계학술대회
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• pp.95-96
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• 2007

Dissolved gas analysis (DGA) is one of the most widely used diagnostic tools for detecting and evaluating faults in electrical equipment. However, interpretation of DGA results is often complex and should always be done with care, involving experienced insulation maintenance personnel. KEPCO (Korea Electric Power Cooperation) has been using DGA technique since KEPCO established the criteria of DGA in 1985. In this paper, we introduce the DGA criteria of KEPCO and analyze the result of DGA. Also we sort pattern in result of DGA. Then, relation between pattern and inner inspection was studied. 67 DGA data was used for analyzing pattern. Some patterns have something to do with cause of incipient fault.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.141-143
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• 2006

Dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-filled electrical equipment. KEPCO make a rule of DGA in 1985. They have been diagnosing power transformer using their DGA criteria. In this paper, we analysis the result of DGA data about transformer in the substation. We try to find out what is cause of an error in DGA diagnosis considering accuracy in extracting gases from mineral oil in transformer. The carbon-monoxide was primary reason of warning in DGA data. We specially consider that aging is a cause of generating of carbon-monoxide in power transformer.

• 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.

• 전기학회논문지P
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• 제58권4호
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• pp.499-504
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• 2009

The analysis for furan compound has provided a complementary technique to dissolved gas analysis(DGA) for monitoring transformers when total concentration of CO and $CO_2$ dissolved in oil only has been evaluated the aging of insulating paper. But, the analysis of furanic compounds by high performance liquid chromatography(HPLC) has been important more than DGA recently. Because it has been known that furanic components in transformer oil have come only from the decomposition of insulating paper. Therefore we have manufactured accelerating aging cell which was aged during 60 hours at 100, 150, 180 and $200^{\circ}C$, respectively, for investigating the characteristics of cellulose paper by thermal using analysis for CO, $CO_2$ and furan compound.

• 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.

• 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.