지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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- 학위논문
- 저자정보
- 지도교수
- 임장섭
- 발행연도
- 2018
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<Abstract>
Power transformers are one of the most important facilities among electric power facilities. When a fault occurs, the effect on the electric power system is significant and due to the increase of electric power demand due to industrial development, the improvement of reliability of electric power transformer and diagnosis technology importance is increasing.
The analysis of gas in gas is one of the most widely used technology among the diagnostic technology of power transformer, and it diagnoses the condition of transformer through analysis of gas generated due to internal defect of transformer.
The analysis of oil gas according to the dissolved gas management standard currently used shows only the types of defects and does not provide specific defects or causes of defects. In addition, due to the narrow space inside the transformer, there are many cases where the abnormal part can not be confirmed during the internal inspection.
This study statistically analyzed 110,000 gas analysis data accumulated from 1983 in korea and analyzed the gas analysis data of the transformer failure and internal inspection, and then applied it to the algorithm of artificial neural network, and identify the defective part of the transformer and the cause of the defect.
Power transformers are one of the most important facilities among electric power facilities. When a fault occurs, the effect on the electric power system is significant and due to the increase of electric power demand due to industrial development, the improvement of reliability of electric power transformer and diagnosis technology importance is increasing.
The analysis of gas in gas is one of the most widely used technology among the diagnostic technology of power transformer, and it diagnoses the condition of transformer through analysis of gas generated due to internal defect of transformer.
The analysis of oil gas according to the dissolved gas management standard currently used shows only the types of defects and does not provide specific defects or causes of defects. In addition, due to the narrow space inside the transformer, there are many cases where the abnormal part can not be confirmed during the internal inspection.
This study statistically analyzed 110,000 gas analysis data accumulated from 1983 in korea and analyzed the gas analysis data of the transformer failure and internal inspection, and then applied it to the algorithm of artificial neural network, and identify the defective part of the transformer and the cause of the defect.
목차
목 차목 차 ⅰList of Tables ⅲList of Figures ⅳList of Terms and Abbreviations ⅵAbstract ⅶⅠ. 서 론 1Ⅱ. 연구배경 32-1. 전력용 변압기 및 유중가스분석 현황 32-2. FMEA(Failure Mode Effect Analysis) 8Ⅲ. 전력용 변압기의 유중가스 분석 123-1. 전력용 변압기의 유중가스 관리기준 133-2. IEC TC 10 데이터베이스 24Ⅳ. 결과 및 고찰 314-1. DGA 데이터의 통계적 해석 314-2. DGA 판정기준의 재현성 확인 414-3. 인공신경망을 이용한 유중가스 패턴 판별 48Ⅴ. 결 론 56참고 문헌 58국문 초록 62
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