Transient Stability Assessment of Power Systems Based on Graph Transformer

XIONG Xueqiao; TIAN Fang; HUANG Yanhao; LI Dongqi; ZHANG Haiyan; MA Chenyu

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Transient Stability Assessment of Power Systems Based on Graph Transformer

XIONG Xueqiao¹, TIAN Fang¹, HUANG Yanhao¹, LI Dongqi¹, ZHANG Haiyan², MA Chenyu¹

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Abstract

【Objective】Aiming at the problems that traditional power system transient stability assessment methods are difficult to handle high-dimensional nonlinear dynamic behaviors and have insufficient efficiency and generalization ability, this paper improves the accuracy and topological adaptability of large-scale power grid transient stability assessment to provide a reliable decision-making basis for power grid emergency control and security defense. 【Methods】An intelligent transient stability assessment method based on Graph Transformer is proposed, which constructs node input features by fusing electrical quantities and fault information at the initial moment of short-circuit faults. It uses graph neural networks to model grid topology, combines Laplacian position encoding to inject structural information, and adopts Transformer multi-head self-attention to capture global complex dependencies among nodes. Multi-topology and multi-fault samples are generated through the PSASP platform, and the model is built with max-pooling for feature aggregation and verified on the IEEE 39-bus system and a regional 1947-bus system. 【Results】Feature ablation experiments on the IEEE 39-bus system show that fault duration is the most important factor affecting transient stability assessment. On the 1947-bus validation set, the model achieves 98.33% accuracy and 97.36% recall, outperforming traditional machine learning and mainstream deep learning models. Under grid topology changes, its performance indicators fluctuate by less than 0.25% and maintain stable and excellent results. 【Conclusions】The proposed method integrates the advantages of graph neural network topology modeling and Transformer global feature capture, with outstanding performance in high-dimensional data processing, complex disturbance adaptation and topological generalization, high accuracy and strong robustness, which can provide reliable decision support for power system emergency control and security defense.

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power systems / transient stability assessment / graph transformer / self-attention mechanism

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XIONG Xueqiao, TIAN Fang, HUANG Yanhao, LI Dongqi, ZHANG Haiyan, MA Chenyu. Transient Stability Assessment of Power Systems Based on Graph Transformer[J]. Electric Power Construction. 0

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