Voltage Stability Monitoring Based on Disagreement-Based Deep Learning in a Time-Varying Environment

The traditional learning based static voltage stability monitoring methods require manual labeling of a large number of training samples. Getting these labeled training sets is expensive and time-consuming in practice. To address this issue, we propose a novel disagreement-based deep learning approach for static voltage stability monitoring. The proposed method trains a deep neural network (DNN) with a small amount of labeled data to monitor the voltage stability. Case studies on the IEEE benchmark systems show that the proposed disagreement-based deep learning achieves more than 94.03% of accuracy with only 300 labeled samples. Moreover, to deal with the time variation of network operating conditions and/or network topology, we further propose a transfer learning method that can adapt the trained DNN to the new environment swiftly. The transfer process only involves a small number of labeled samples in the new environment. Our case studies show that after the network topology changes due to line tripping, the transferred DNN achieves more than 95.48% of accuracy in the new environment by utilizing only 5 labeled samples for each category.