메뉴 건너뛰기
소속 기관 / 학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
고객센터 ENG
주제분류

논문 기본 정보

저자정보
(KEPCO Research Institute) (KEPCO Research Institute) (KEPCO Research Institute) (KEPCO Research Institute)
저널정보
대한전기학회 전기학회논문지 전기학회논문지 제71권 제2호
오류 신고하기

피인용 0

검색

    초록·키워드

    Recently, with the development of deep learning technology, failure analysis and failure diagnosis research using image analysis of objects have been actively conducted. In particular, research on algorithm and system development for diagnosing facilities using drone photographed images is being applied to the industrial field. The results are reaching the level of commercialization. In the electricity field, drone images have been used in the field of power facility diagnosis since two to three years ago. There are not many abnormal learning data to determine whether transmission facilities are abnormal, so full-scale use in the actual field is limited. Therefore, this study proposes a method of securing more learning data by utilizing images of limited failure data. In addition, the obtained data is used for learning to present deep learning methods and research results for developing a more accurate transmission facility diagnosis system. As a result of this study, it was confirmed that the average precision was improved by about twice from 39.2% to 81.1% by applying the learning model technology. This improved method of artificial intelligence learning technology is expected to prevent power transmission failure in advance, avoid power outage costs caused by failure, and reduce maintenance costs through inspection automation.

    최근 본 자료 전체보기