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
Module-level anomalies in photovoltaic plants may be obscured by aggregate indicators, with maintenance labels often being unavailable. We define Normal, LowPower, and Thermal Anomaly weak labels using same-string peer deviations and two-sample persistence. Thermal Anomaly denotes peer-relative overtemperature, not a thermographically confirmed cell-level hotspot. We analyzed 15 months of 15-min measurements collected from 349 modules. A 55℃ comparison threshold flagged 347 modules at least once and yielded no winter detections, whereas the peer-relative criterion reduced the number of flagged modules to 143 and mitigated seasonal bias. Module-wise five-fold cross-validation prevented identity leakage, and we evaluated 16 configurations combining directionality, Self-Attention, position-wise feed-forward blocks, and temporal pooling. Across 976,911 pooled out-of-fold windows, Macro F1 was 0.9837, and the fold mean was 0.9544 ± 0.0373. Our findings reveal weak-label consistency on unseen modules, not accuracy against field-confirmed faults. The unidirectional Long Short-Term Memory (LSTM) with mean pooling underperformed, whereas changing any one factor recovered most of the performance. Moreover, other configurations did not differ substantially, supporting deployment-specific complexity selection.
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.