Self-adaptive Power System Transient Stability Prediction Based on Transfer Learning

Transient stability prediction of power systems based on artificial intelligence usually requires training a prediction model with a large number of samples generated offline, and then performing online prediction based on the real-time response of the system. However, when the system's operating mode and topological structure change greatly, the accuracy of the prediction model will decrease significantly. Therefore, a self-adaptive transient stability prediction method that can track the changes of the system is urgently needed. Considering this problem, transfer learning is introduced into transient stability prediction, and a self-adaptive prediction method is proposed based on convolutional neural networks. Firstly, a pre-trained model is obtained based on the convolutional neural network by training a large number of samples generated at the offline stage. When the operating mode and the topological structure change greatly, the network structure of the pre-trained model can be kept unchanged and the network parameters in the two convolutional layers, the two pooling layers, and the fully connected layer are transferred into the new model. A variable step dataset generation method is used to retrain the parameters of classification layer with a minimum balanced dataset so that an updated model can be achieved quickly. Experiment results in the New England 10-machine 39-bus system demonstrate that the proposed method can effectively update the prediction model and reduce training time dramatically. It provides a new idea for self- adaptive prediction of power system transient stability based on artificial intelligence. © 2020, Power System Technology Press. All right reserved.