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
Recent fire incidents in large-scale ESS(energy storage system) have raised concerns about the electrical risks inherent in their operation. Specifically, the CMV(common mode voltage) generated by the PCS(power conversion system) in ESS can impose continuous electrical stress on the battery and DC circuits, potentially leading to severe issues such as battery insulation failure and increased electrical noise. CMV primarily arises from interactions between switching components and parasitic capacitance, and this phenomenon becomes more pronounced in large-scale ESS, accelerating battery degradation and increasing the risk of fire hazards. This study analyzes the mechanisms of CMV generation in ESS and quantitatively evaluates CMV variations during charging and discharging processes through linear regression analysis. By modeling the relationship between ESS output power and CMV, system degradation and potential risks are predicted by examining CMV fluctuations during operation. Experimental results indicate that as charging power increases, the CMV magnitude also rises, intensifying electrical stress on the battery and DC circuits, while CMV decreases during discharging. These findings suggest that CMV trends can serve as indicators for early detection of system abnormalities by comparing them with trends observed in healthy systems. This approach has the potential to enhance ESS safety and help prevent incidents such as fires by identifying potential risks in advance.
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.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-25-02-092315966
