A Method for Power System Transient Stability Assessment Based on Transfer Learning

Traditional machine learning models are subject to changes in the data distribution. Once the topology of power grid changes, the distribution of power grid data will change, resulting in the consequence that the former transient stability assessment model based on machine learning is no longer applicable and needs to be retrained. However, the power network after the topology changes cannot provide enough data for the training of the new model. To solve the problem above, this paper proposes a method of power system transient stability assessment based on transfer learning, which fully taps the potential of past data and assists a small amount of newly acquired data in training the model. The simulation results on the CEPRI36 system show that the proposed method can effectively improve the accuracy of original classification models, especially for weak classifiers with insufficient generalization ability. The disadvantage of transfer learning lies in the long training time, which still needs to be improved.