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.
초록·키워드
Countries are looking for a pathway toward a sustainable transition from fossil-based to less or zero-carbon sources in energy sector in order to reduce its impact from climate change. Among different renewable resources, photovoltaic power system (PV) is considered as one of the most promising technology, which has the biggest potential for increasing renewable energy. However, its profit can be varied depending on operation and management, which possibly causes performance degradation and safety issues due to faults. Therefore, we have collected actual operation data from the PV monitoring system which located in Changwon, Gyeong-nam province during one year. While most of the PV system has security function which is limited to its inverter protection, in this study, based on fault data software program is developed for fault diagnosis in order to increase robustness of the PV system and minimize operation cost. Also, the program comprises machine learning algorithm based on fault data to classify its types of faults. It also presents economic loss of each PV module considering mean time to repair (MTTR) occurred from the event of faults in the PV system. As a result, the program helps faster fault diagnosis of the PV system and decreasing overall operation cost for the system operator.
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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UCI(KEPA) : I410-ECN-0101-2020-560-001579627
