  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 F_1-score of 98.9%.
