Adaptive assessment of transient stability for power system based on transfer multi-type of deep learning model

Aiming at the problems that the accuracy and generalization ability are unstable when different types of artificial intelligence networks apply in transient stability assessment of power system，the assess⁃ ment accuracy reduces when the operation mode or topological structure changes greatly，and the time and effort are wasted when retraining a new model，an adaptive assessment method integrating with a transfer multi-type of deep learning model（tmDLM） is proposed，which integrates with three different deep learning models of deep belief network，convolutional neural network and long short-term memory network. Each type of the trained deep learning model is taken as the source domain model，when the operation mode or the topological structure changes largely，a small number of the object domain sample sets are used to fine-tuning the pre-trained model，making it quickly track the current operation state of the system，then the tmDLM is obtained. The simulative results of New England 10-machine 39-bus system and the Central China Power Grid show that the proposed method can make full use of the advantages of each type of deep learning method with good generalization ability，the six-classification model can assess the stability margin／instability degree while judging the stable state，the deep learning model has good assessment accu⁃ racy and timeliness after transfer learning，which greatly reduces the update time of the model and realizes self-adaptive assessment of transient stability of power system. © 2023 Electric Power Automation Equipment Press. All rights reserved.