基于图Transformer的电力系统暂态稳定判别

熊学侨; 田芳; 黄彦浩; 李东琦; 张海岩; 马辰宇

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电力建设 ›› 0

基于图Transformer的电力系统暂态稳定判别

熊学侨¹, 田芳¹, 黄彦浩¹, 李东琦¹, 张海岩², 马辰宇¹

作者信息 +

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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文章历史 +

摘要

【目的】针对传统电力系统暂态稳定判别方法难以处理高维非线性动态行为、效率与泛化能力不足的问题,提升大电网暂态稳定判别精度与拓扑适应性,为电网紧急控制与安全防御提供可靠决策依据。【方法】提出基于图Transformer的电力系统暂态稳定智能判别方法,融合短路故障起始时刻电气量与故障信息构建节点输入特征;采用图神经网络建模电网拓扑关系,结合拉普拉斯位置编码注入结构信息,利用Transformer多头自注意力机制挖掘节点间全局复杂依赖关系;通过PSASP平台生成多拓扑、多故障场景样本,采用最大池化聚合节点特征完成模型构建,并在IEEE 39节点与某区域1947节点系统开展试验验证。【结果】IEEE 39节点特征消融实验表明,故障持续时间是影响暂态稳定判别最主要的因素;在1947节点系统验证集上,模型准确率达98.33%、召回率97.36%,综合性能优于传统机器学习与主流深度学习对比模型;在电网拓扑变化场景下,模型各项性能指标波动小于0.25%,保持稳定且优异的判别效果。【结论】所提方法兼具图神经网络拓扑建模与Transformer全局特征捕捉优势,在高维数据处理、复杂扰动适应与拓扑泛化方面性能突出,判别准确、鲁棒性强,可为电力系统紧急控制与安全防御提供可靠的决策依据。

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.

导出引用

熊学侨, 田芳, 黄彦浩, 李东琦, 张海岩, 马辰宇. 基于图Transformer的电力系统暂态稳定判别[J]. 电力建设. 0

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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