Short-Term Voltage Stability Assessment Based on Heterogeneous Edge-Integrated Graph Attention Network

Traditional data-driven models have limited capabilities to describe topological relations, leading to difficulties in short-term voltage stability (STVS) assessment with strong locality. For the real-time dynamic security analysis (DSA), a novel STVS assessment method based on the heterogeneous edge-integrated graph attention network is proposed. Considering various credible contingencies, the STVS quantitative indicators of buses are obtained, avoiding the time-consuming problem of time-domain simulation in the conventional DSA. First, the mechanism similarity between the STVS and message passing-based graph neural network is analyzed. A virtual homomorphism technique and multi-layer perceptron are introduced to handle the original heterogeneous input features. Then, to focus on the nonlinear impact of transmission lines on dynamic voltage interactions, an edge feature integration method is designed for feature aggregation. The physical processes of STVS in the system under line contingencies can be effectively reflected. Finally, case studies verify the superiority of the proposed method in terms of both accuracy and its generalization ability to new topologies. To understand the mechanism of the model, a post hoc interpretability analysis is conducted based on the attention weight and quasi-steady state sensitivity at the node and feature levels, respectively.