Static information, K-neighbor, and self-attention aggregated scheme: a transient stability prediction model with enhanced interpretability

Abstract

Data-driven preventive scanning for transient stability assessment (DTSA) is a faster and more efficient solution than time-domain simulation (TDS). However, most current methods cannot balance generalization to different topologies and interpretability, with simple output. A model that conforms to the physical mechanism and richer label for transient stability can increase confidence in DTSA. Thus a static-information, k-neighbor, and self-attention aggregated schema (SKETCH) is proposed in this paper. Taking only static measurements as input, SKETCH gives several explanations that are consistent with the physical mechanisms of TSA and provides results for all generator stability while predicting system stability. A module based on the self-attention mechanism is designed to solve the locality problem of a graph neural network (GNN), achieving subgraph equivalence outside the k-order neighborhood. Test results on the IEEE 39-bus system and IEEE 300-bus system indicate the superiority of SKETCH and also demonstrate the rich sample interpretation results.