A graph attention networks-based model to distinguish the transient rotor angle instability and short-term voltage instability in power systems

Abstract

Digital simulation is significant for the operating mode and control decision-making of power systems. In the process of simulation data analysis, stability analysis is an essential part. One of the most challenging tasks is to distinguish between transient rotor angle instability and short-term voltage instability. This paper proposes a graph attention networks (GATs)-based method to overcome this ticklish problem via integrating power grid topology information into the neural networks. Compared with the conventional graph convolutional networks (GCNs), the attention mechanism is introduced into the GATs to learn the weights among different neighbor vertices in the graph. Due to the difficulty of distinguishing between the rotor angle instability and voltage instability in some samples, a label-smoothing method is adopted to alleviate the influence caused by label inaccuracy. Case studies are conducted on an 8-machine 36-bus system and Northeast China Power System. Simulation results show that the proposed method has better performance than conventional GCNs and other machine learning methods.