Power System Transient Stability Assessment Based on Spatio-Temporal Broad Learning System

The safety, reliability and rapidity of the transient stability assessment (TSA) method are important for improving the reliability and security of power system operation. TSA methods based on deep learning have good evaluation performance, but the increasing number of network layers leads to the model training time being continuously extended. Therefore, a TSA method based on broad learning system (BLS) is proposed to improve the efficiency of power system TSA. Meanwhile, in order to overcome the deficiency of BLS in feature extraction, a TSA model based on spatio-temporal broad learning system (STBLS) is established, which combines BLS with graph convolutional network (GCN) and temporal convolutional network (TCN). First, the grid topology map and electrical measurement data are used as model inputs, and the graph convolution module and the temporal convolution module are used to extract the spatial features and temporal features of the system, respectively. The extracted spatio-temporal features are then used as inputs to the BLS classification module, which is utilized for the rapid assessment of transient stability. Simulation results demonstrate the effectiveness of the proposed model and highlights its superiority in terms of training time. Note to Practitioners—This paper is motivated by the realiza-tion of a fast and accurate assessment of power system transient stability (TSA). The main goal is to shorten the training time of the model as much as possible while ensuring that the assessment model has a high accuracy. In this paper, the broad learning system (BLS) is introduced into the field of TSA, and a TSA model based on spatio-temporal broad learning system (STBLS) is proposed by combining the BLS with graph convolutional net-work (GCN) and temporal convolutional network (TCN), which greatly shortens the training time of the model. Experimental results show that this method has higher efficiency and practical application value. In future research, we will conduct more com-prehensive validation of such methods and further optimize the model to improve its stability and generalization ability.