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

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

• 한국전기전자재료학회논문지
• /
• 제28권4호
• /
• pp.238-243
• /
• 2015

The vegetable insulating oils are substitute for the mineral oil in power transformer. Vegetable insulating oils has higher flash/fire point and biodegradability than conventional mineral oils. In this paper, we investigated the dissolved gas analysis of vegetable oils. In the experiment, I had to accelerated aging under the same conditions mineral oil and vegetable oils. Accelerated aging proceeded to about 100% of the life of oil-filled transformer. In addition, we performed gas analysis of insulating oil of accelerated aging progress. The experiment results of the five gases was measured with the exception of Hydrogen and Acetylene. The mineral oil and vegetable oils gas is generated in a similar tendency depending on the accelerated aging. As a result, vegetable oils, can be dissolved gas analysis by method such as mineral oil.

• 전기학회논문지P
• /
• 제60권3호
• /
• pp.114-119
• /
• 2011

Large Power transformer is a complex and critical component of power plant and consists of cellulosic paper, insulation oil, core, coil etc. Insulation materials of transformer and related equipment break down to liberate dissolved gas due to corona, partial discharge, pyrolysis or thermal decomposition. The dissolved gas kinds can be related to the type of electrical faults, and the rate of gas generation can indicate the severity of the fault. The identities of gases being generated are using very useful to decide the condition of transformation status. Therefore dissolved gas analysis is one of the best condition monitoring methods for power transformer. Also, on-line multi-gas analyzer has been developed and installed to monitor the condition of critical transformers. Rogers method, IEC method, key gas method and Duval Triangle method are used to failure diagnosis typically, and those methods are using the ratio or kinds of dissolved gas to evaluate the condition of transformer. This paper analyzes the reliability of transformer diagnostic methods considering actual dissolved gas concentration. Fault diagnosis is performed based on the dissolved gas of five transformers which experienced various fault respectively in the field, and the diagnosis result is compared with the actual off-line fault analysis. In this comparison result, Diagnostic methods using dissolved gas ratio like Rogers method, IEC method are sometimes fall outside the ratio code and no diagnosis but Duval triangle method and Key gas method is correct comparatively.

• 대한전기학회논문지:전기물성ㆍ응용부문C
• /
• 제54권5호
• /
• pp.206-212
• /
• 2005

In this paper, criterion and algorithm for on-line dissolved gas of a Power transformer are studied. For the initial diagnosis of a power transformer, the on-line dissolved gas analysis is one of the most important and acceptable item to preventively diagnose a power transformer. But the criterion and algorithm of this item are not established yet in korea. In this paper, criterion and alarm level of the on-line dissolved gas analysis are based on the analysis of on-line data of operating transformers, Korea industrial standard and operation manual for a power transformer as well as accumulated data of the preventive diagnosis systems which have been operated at nine substations of Korea Electric Power Co.(KEPCO) since 1997, Therefore, the criterion and alarm level proposed in this paper are to be well suitable and are adaptable for the domestic operational environments and conditions of the power transformer. Considering that the conventional diagnosis system is capable only of accumulating and monitoring data of the power transformer operation, the criteria and the algorithms make it possible to accomplish an ultimate goal of the preventive diagnosis system. It is expected, therefore, that they will have a beneficial effect on broad applications of the preventive diagnosis system and the achievement of manless substation system in the future.

• 예술인문사회 융합 멀티미디어 논문지
• /
• 제8권3호
• /
• pp.563-570
• /
• 2018

본 논문에서는 기계학습 기반의 효율적인 변압기 유중가스 분석 및 분류 방법을 제안하여 기존 IEC 60599 진단기준 기반의 문제점을 해결하고 진단 성능을 개선한다. 기존 IEC 60599 진단기준은 조성비가 진단 기준에 존재하지 않거나 경계조건에 있는 경우 진단 전문가에게 의뢰하지 않고는 해석에 어려움이 있으며 진단영역이 겹치는 부분이 존재하므로 정확한 원인분석을 수행하는 데에 한계가 있다. 따라서 IEC 60599 진단 기준만으로 변압기 유중가스 데이터를 분석 및 분류하는 경우 IEC 60599 기준에 만족하지 않는 데이터를 분류하지 못한다는 문제점이 있다. 이와 같은 문제를 해결하기 위해 기계학습 기반의 변압기 유중가스 분석 및 분류 방법을 제안하였다. 제안한 기계학습 기반의 변압기 유중가스 분석 방법은 IEC 60599 진단기준으로 판단이 불가능한 데이터를 서포트 벡터 머신을 통해 정확히 분류 할 수 있다. 제안한 방법의 성능을 검증하기 위해 실제 유중가스 데이터를 사용하여 기계학습 기반의 변압기 유중가스 분석 방법의 효율성을 검증하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 추계학술대회 논문집 학회본부A
• /
• pp.301-303
• /
• 1998

This paper presents a new approach to diagnose and detect faults in oil-filled power transformers based on various dissolved gas analyses. A theoretic fuzzy information model is introduced, An inference scheme which yields the 'most' consistent conclusion proposed. A framework is established that allows various dissolved gas analyses to be combined in a systematic way such as the Dempster-Shafer rule. Good diagnosis accuracy is obtained with the proposed approach.

• 한국지능시스템학회논문지
• /
• 제7권1호
• /
• pp.45-53
• /
• 1997

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.

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

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