A hybrid transfer learning method for transient stability prediction considering sample imbalance

Data-driven transient stability prediction (TSP) exists with issues of model robustness and sample imbalance. An instance-based and parameter-based of hybrid transfer learning (HTL) method for TSP considering sample imbalance is proposed to address these two issues. The instance-based transfer learning is firstly utilized to select those applicable samples from the source domain system when the boundary conditions such as the network topology and the operational mode of power system change, which may significantly shorten the time for time-domain simulation (TDS) of the target domain system. Subsequently, the conditional generative adversarial network (CGAN) is employed to augment the unstable samples for obtaining a more balanced training data set. Finally, the parameter-based transfer learning is adopted to quickly update the model for TSP. Case studies conducted on a New England 10-machine 39-bus system, an IEEE 50-machine 145-bus system and a Western Electricity Coordinating Council (WECC) 29-machine 179-bus system demonstrate superior quality and diversity of the generated samples obtained by the CGAN-based data augmentation algorithm than the ones of traditional methods involving SMOTE, ADASYN, GAN and DCGAN. Furthermore, the results from numerous numerical experiments also indicate that the proposed HTL method considering sample imbalance improves the model robustness of TSP.