Semi-supervised learning for fault identification in electricity distribution networks

Abstract

The detection and identification of faults in electricity distribution networks is essential in improving the reliability of power supply. After observing many fault current signals we found that: (1) features of many recorded fault electrical signals were unknown or obscure; (2) the fault types of most sample signals had no clear definition, that is, the labeled sample were very limited. In this situation, the semi-supervised support vector machine (S3VM) and SVM active learning were firstly introduced to distinguish the short circuit and grounding in distribution networks. We used wavelet packet analysis to extract features based on energy spectrum as the physical features of electric signals, then some statistical characteristics were also computed and selected to form a mixed feature set. A case study was conducted on a real data set including 72 labeled and 7720 unlabeled electrical signals for fault diagnosis. By performing transductive support vector machine (TSVM) and SVM active learning with mixed features, our experimental results showed that both of the two models can effectively identify the fault types. Meanwhile, the accuracy of TSVM is higher than that of SVM active learning.