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
This paper proposes a multimodal data-based deep learning model for partial discharge detection in solid- and liquid-insulated electrical equipment to support early fault diagnosis and electrical accident prevention. Existing approaches often rely on a single data modality, which may limit their ability to capture complementary characteristics of different partial discharge types. The proposed model independently extracts spatial features from phase-resolved partial discharge (PRPD) images and temporal features from time-series signals, and combines them through Late Fusion for final classification. Experiments were conducted using 60,000 multimodal samples acquired from ACSR-OC with solid insulation and an oil-immersed power transformer with liquid insulation, covering normal, noise, void discharge, surface discharge, and corona discharge states. The training, validation, and test sets were constructed without overlapping identical files, and experiments were repeated using five random seeds. Compared with ResNet-18, LSTM-AE, and Cross-Attention Transformer, the proposed model achieved the highest average performance, with an Accuracy of 99.4733 ± 0.3301% and a Macro F1-score of 99.4716 ± 0.3315%. These results show that combining complementary spatial and temporal information can improve partial discharge detection performance and contribute to more reliable electrical equipment condition diagnosis.
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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