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

• Journal of Electrical Engineering and Technology
• /
• 제3권3호
• /
• pp.293-299
• /
• 2008

One of the problems with the fault diagnosis of transformers based on dissolved gas is the inability to match the result of the different standards of fault diagnosis with real world standards. In this paper, the results of the different standards are analyzed using fuzzy logic and then compared with the empirical test. The proposed method is based on the standards and guidelines of the International Electrotechnical Commission (IEC), the Central Electric Generating Board (CEGB), and the American Society for Testing and Material (ASTM) and its main task is to assist the conventional gas ratio method. The comparison between the suggested method and existing methods indicates the capability of the suggested method in the on-line fault diagnosis of transformers. In addition, in some cases the existing standards are not able to diagnose the fault. For theses instances, the presented method has the potential of diagnosing the fault. In this paper, the information of three real transformers is used to show the capability of the suggested method in diagnosing the fault. The results validate the capability of the presented method in fault diagnosis of the transformer.

• 한국ITS학회:학술대회논문집
• /
• 한국ITS학회 2008년도 제7회 추계학술대회 및 정기총회
• /
• pp.559-564
• /
• 2008

• Journal of Korean Neurosurgical Society
• /
• 제49권6호
• /
• pp.384-386
• /
• 2011

A foraminal gas pseudocyst is a rare cause of lumbar radiculopathy. The association with a sudden foot drop has not been previously reported. Here, a 67-year-old woman with sudden foot drop on the left side is reported. Computed tomography and magnetic resonance imaging identified a foraminal gas containing lesion compressing the left L5 root at the L5-S1 foramen. The foraminal gas containing lesion compressing the L5 ganglion was successfully removed by the posterior approach. The histological diagnosis was a gas pseudocyst. This unique case of surgically proven gas pseudocyst indicates that it should be included in the differential diagnosis of patients presenting with sudden foot drop.

• 한국지능시스템학회논문지
• /
• 제14권1호
• /
• pp.1-8
• /
• 2004

본 논문에서는 실내대기 가스모니터링 시스템에서의 센서 고장 진단을 위한 신경회로망 기반 고장진단방법을 제안한다. 제안한 고장진단 방법에서는 신호패턴추출을 위해 센서히터 온도조절방법을 이용하였으며, 분류를 위해서는 ART2 신경회로망을 이용하였다. 그리고 가스모니터링 시스템의 실제 데이터를 이용한 시뮬레이션을 통해 제안한 ART2 신경회로망 기반 센서고장진단방법의 성능과 유용성을 확인하였다.

• 한국가스학회지
• /
• 제27권2호
• /
• pp.86-94
• /
• 2023

본 연구는 공동주택에 도시가스를 공급하기 위해 정압기 대신 설치하는 공용 압력조정기의 안전성 향상을 위한 대안을 마련하기 위한 목적으로 진행되었다. 먼저, 국내 압력조정기의 안전기준 및 설치 실태를 조사하여 문제점을 도출하고 해결방안을 제시하였다. 또한 정량적 위험성평가 솔루션인 DNV Phast-Safeti 프로그램을 이용해 공동주택에 설치되는 압력조정기와 주변 가스설비에 내포된 가스누출 등의 위험성을 개인적 위험도와 사회적 위험도로 구분하여 평가하고, 압력조정기의 설치와 관리 측면에서 위험성 감소를 위한 방안을 도출하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 하계학술대회 논문집 A
• /
• pp.622-624
• /
• 2004

To reduce the effect of fault on power systems, like GIS and transformers, power system authorities are using various technologies to monitor and diagnose there facilities. Developing On-Line monitoring systems by using IT technology is main issue of nowadays for power system authorities. Among various monitoring and diagnosis technologies, gas sensing technologies can be most useful candidate because large power systems are using gas and oils for there insulation and analyzing density of gases that are included in the gas and oils for insulation purpose tell us what kind of reaction were arisen. In this paper, we describe on the gas sensing technology that are used for power systems monitoring and diagnosis.

• 대한전기학회논문지:전기물성ㆍ응용부문C
• /
• 제55권6호
• /
• pp.308-312
• /
• 2006

Partial Discharge (PD) phenomena occurred by different nature of insulating defects has been regarded as a random process by which Phase Resolved Partial Discharge Analysis(PRPDA) has been proposed and then commercially accepted for the diagnosis of the power apparatus since more than three decades. Moreover, for the same purpose, a novel approach based on the Chaotic Analysis(CAPD) has been proposed since 2000, in which Partial Discharge(PD) phenomena is suggested to be considered as a deterministic dynamical process. In this work, for the diagnosis of Gas Insulated Transformer(GITr), four different types of specimen were fabricated as a model of the possible defects that might possibly cause its sudden failures such as turn to turn insulation, inter coil insulation, free moving particle and protrusion. For this purpose, these defects are introduced into the Gas Insulated Transformer(GITr) mock-up and experimental investigations have been carried out in order to analyze the related Partial Discharge(PD) patterns by means of both Phase Resolved Partial Discharge Analysis(PRPDA) and Chaotic Analysis(CAPD) respectively and then their comparisons are made systematically.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 2004년도 학술대회 논문집
• /
• pp.87-90
• /
• 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
• /
• 제10권4호
• /
• pp.1765-1772
• /
• 2015

The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.