인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
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
Series arc faults occurring at the AC output and DC input sides of a three-phase pulse-width modulation inverter can degrade system reliability and increase the risk of electrical accidents. Because the effects of an arc on the output-current waveform vary depending on the fault location and operating condition, the diagnostic characteristics of AC and DC series arcs should be evaluated separately. In this study, the performance of several artificial-intelligence models was compared using a single A-phase output-current signal measured under various inverter operating conditions. The measured current was used as a time-domain sequence and was also transformed into a frequency-domain magnitude spectrum using the fast Fourier transform. The experimental results showed that the CNN-Transformer achieved the highest diagnostic accuracy for both AC and DC arc faults with both input representations. In particular, the frequency-domain input provided high precision and recall for both AC and DC arc conditions. These results demonstrate that both AC output-side and DC input-side series arcs can be effectively diagnosed from the output-current characteristics, while the diagnostic performance depends on the fault location, input representation, and model structure.
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.