2019/11/09 by Kui Wang, Jian Sun, Wang, Kui +5
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Electrical engineering #Geotechnical Engineering and Underground Structures #Islanding Detection in Power Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Systems and Control (eess.SY) #Vibration and Dynamic Analysis #cs.LG #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1911.04467
arxiv created 2019/11/09 · openalex publication_date 2019/11/09 · arxiv updated 2019/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Conductor galloping is the high-amplitude, low-frequency oscillation of overhead power lines due to wind. Such movements may lead to severe damages to transmission lines, and hence pose significant risks to the power system operation. In this paper, we target to design a prediction framework for conductor galloping. The difficulty comes from imbalanced dataset as galloping happens rarely. By examining the impacts of data balance and data volume on the prediction performance, we propose to employ proper sample adjustment methods to achieve better performance. Numerical study suggests that using only three features, together with over sampling, the SVM based prediction framework achieves an F1-score of 98.9%.