A combinational transfer learning framework for online transient stability prediction. (June 2022) - Cui, Han; Wang, Qi; Ye, Yujian; Tang, Yi; Lin, Zizhao
Abstract: Data-driven methods have been intensively investigated in transient stability prediction due to the advantages on speed and accuracy. However, the variability of power systems disables the well-trained model when the contingencies or operation points are not covered in original training set. To address this issue, this paper proposes a combinational transfer learning framework to update transient stability prediction model in time-varying power systems, where convolutional neural network (CNN) is selected as the classifier. An innovative sample transfer algorithm is proposed to select applicable samples from source system, which decreases the time for time-domain simulation. Meanwhile, different model transfer schemes are compared for better accuracy and training efficiency of CNN. Test results on IEEE 39-bus system and an actual power grid verifies the efficiency and scalability of the proposed method. In addition, it performs well in the imbalanced training set and data with random noise.