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
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.9
- 수록면
- 1,018 - 1,028 (11page)
- DOI
- 10.5370/KIEE.2023.72.9.1018
이용수
초록· 키워드
오류제보하기
In power systems, faults, such as ground faults and short circuits, and non-fault disturbances, such as large load fluctuations and unbalances, occur frequently. However, the power system responses to power faults and non-fault disturbances are different. Therefore, it is essential to accurately distinguish between faults and non-fault disturbances in power systems. Previous studies have collected large-scale data by monitoring real-time parameters of the power system and detecting the occurrence of power system faults. However, this study does not focus only on diagnosing power system faults but also on accurately distinguishing between faults and non-faults disturbances and uses various classification models to train the data and evaluate and analyze the prediction results. Collecting power system parameters when faults and non-faults disturbances occur is not easy. Therefore, this study used DIgSILENT PowerFactory software to simulate faults and non-faults disturbances in the power system and collected 120 small data sets. The data collected for the 120 cases consists of various metrics such as voltage, current, frequency, rotor speed, and HVDC parameters. This study used seven classification models for training and prediction: decision tree, gradient boosting classifier, k-nearest neighbors, logistic regression, naive Bayes classification, and random forest regression. In addition, this study introduced an importance-based data reorganization method to improve the performance of the best-performing classification model and analyzed its effectiveness.
목차
Abstract
1. 서론
2. 본론
3. 사례 연구
4. 결론
References
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