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.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
In response to the problem of insufficient accuracy and diagnostic efficiency of traditional transmission line fault diagnosis models, this study proposed an improvement of region-based convolutional neural networks based on fuzzy logic algorithm and constructed a transmission line fault diagnosis algorithm. And based on this, a transmission line fault diagnosis model was constructed. The effectiveness of the proposed fault diagnosis algorithm was evidenced, and the accuracy, precision, and F1 value of the algorithm were 98.7%, 87.2%, and 0.81, respectively, which was higher than other comparative algorithms. Besides, the study also validated the effectiveness of the transmission line fault diagnosis model integrating fuzzy theory, and found that the accuracy of the model was 0.94, the loss function value was 0.06, the precision was 96.2%, the mean absolute error was 1.64 ∗ 10<sup>−2</sup>, and the root mean square error value was 1.78 ∗ 10<sup>−2</sup>, which was better than other comparative models. In summary, the transmission line fault diagnosis model that integrates fuzzy theory and improved region-based convolutional neural network has better diagnostic accuracy and efficiency than traditional models. The diagnostic model proposed in the study can quickly and accurately diagnose faults in transmission lines, thereby prompting maintenance personnel to take timely measures to reduce the impact of faults on the power system, improve system stability and reliability.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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