Conductor Galloping Detection on Imbalanced Dataset: SVM with Smart Sampling

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 detection framework for conductor galloping. The difficulty comes from limited and imbalanced dataset as galloping happens rarely. By examining the impacts of data balance and data volume on the detection 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 detection framework achieves an F1-score of 98.9%.