南方电网技术2025，Vol.19Issue(1)：63-73,92,12.DOI:10.13648/j.cnki.issn1674-0629.2025.01.007
基于潮流嵌入和最小割池化的电网静态安全分析图学习模型
Graph Learning Model of Power System Static Security Analysis Based on Power Flow Embedding and Min-Cut Pooling
摘要
Abstract
The application of data-driven models for rapid static security analysis in new power system is a research area worth explor-ing.Enhancing the generalization ability of data-driven models to operation condition changes and the adaptability to power system topology variations is one of the key technical challenges.A graph learning model for static security analysis of the power system based on power flow embedding and the min-cut pooling is proposed.At first,a power flow embedding module directed by the node voltage restoration is designed to improve the model's generalization ability,which converts the topological differences in N-1 contingency scenarios into node feature differences.Secondly,based on the concept of community partitioning,a min-cut pooling technology is employed to dynamically reduce node scale and node feature dimensions,which enables the model to adapt to topologi-cal changes.Verification tests and visualization analyses conducted on IEEE 39-bus and IEEE 118-bus systems demonstrate that the model can achieve high accuracy,second-level evaluation speed,and good adaptability to variation of the power grid scale and topology.关键词
静态安全分析/图深度学习/掩模图自编码器/潮流嵌入/图池化/拓扑变化适应性Key words
static security analysis/graph deep learning/masked graph auto-encoder/power flow embedding/graph pooling/adapt-ability to the topology variation分类
信息技术与安全科学引用本文复制引用
马遵,李永哲,何鑫,管霖,向川,陈勇,何伊慧..基于潮流嵌入和最小割池化的电网静态安全分析图学习模型[J].南方电网技术,2025,19(1):63-73,92,12.基金项目
国家自然科学基金资助项目(52077080) （52077080）
云南电网有限责任公司科技项目(056200KK52220044).Supported by the National Natural Science Foundation of China(52077080) （056200KK52220044）
the Science and Technology Project of Yunnan Power Grid Co.,Ltd.(056200KK52220044). （056200KK52220044）
