인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
Transformers are essential components in power systems, and their reliability must be ensured through timely maintenance. Conventional inspection and maintenance practices often follow fixed schedules regardless of the transformer’s actual condition, which may result in unnecessary costs or delayed detection of potential failures. To address this issue, this study proposes an optimized maintenance strategy based on dissolved gas analysis (DGA) of transformer insulating oil, focusing on carbon monoxide (CO) and carbon dioxide (CO₂) concentrations as key indicators of insulation degradation. By analyzing inspection records and statistical data, four condition-based inspection cases are developed and compared with the existing periodic maintenance standards adopted by utilities such as KEPCO. The analysis evaluates inspection intervals, labor costs, and cost-saving potential while considering the effectiveness of detecting insulation deterioration. Results show that condition-based inspection using CO and CO₂ thresholds can reduce unnecessary inspections and achieve significant cost savings, while also contributing to transformer life extension and improved system reliability. This study demonstrates that incorporating DGA into inspection scheduling provides a more efficient and economical maintenance framework compared to conventional time-based approaches. The findings highlight the importance of adaptive maintenance strategies for enhancing asset management and ensuring stable power supply.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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