Conductor Galloping Prediction on Imbalanced Datasets: SVM with Smart Sampling
Signal Processing
2019-11-13 v1 Machine Learning
Systems and Control
Systems and Control
Machine Learning
Abstract
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%.
Cite
@article{arxiv.1911.04467,
title = {Conductor Galloping Prediction on Imbalanced Datasets: SVM with Smart Sampling},
author = {Kui Wang and Jian Sun and Chenye Wu and Yang Yu},
journal= {arXiv preprint arXiv:1911.04467},
year = {2019}
}