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融合DBSCAN与GAN集成学习的电力系统安全评估与边界生成方法

刘锡凯 薛文雅 曾沅 任郡枝 董向明 李良浩

电力系统及其自动化学报2026,Vol.38Issue(6):46-55,10.
电力系统及其自动化学报2026,Vol.38Issue(6):46-55,10.DOI:10.19635/j.cnki.csu-epsa.001800

融合DBSCAN与GAN集成学习的电力系统安全评估与边界生成方法

Power System Security Assessment and Boundary Generation Method Integrating DBSCAN and GAN Ensemble Learning

刘锡凯 1薛文雅 1曾沅 1任郡枝 1董向明 2李良浩2

作者信息

  • 1. 天津大学电气自动化与信息工程学院,天津 300072
  • 2. 国家电网公司华中分部,武汉 430077
  • 折叠

摘要

Abstract

In the context of hybrid AC/DC power grids,the increasingly complex operating conditions and growing un-certainties of a system have imposed limitations on the traditional thermal security region(THSR)analysis method,making it difficult to accurately handle operation scenarios where multiple transmission lines simultaneously approach their stability limits.In this paper,a fast security boundary generation method integrating density based spatial cluster-ing of applications with noise(DBSCAN)and ensemble learning is proposed.First,it classifies the operating data ac-cording to line load status using DBSCAN clustering and constructs a set of security boundaries corresponding to differ-ent load levels.Second,a boundary selection model is built with the improved AdaBoost.M2 algorithm as the core,which outputs potential over-limit boundaries with high confidence and thus avoids the misjudgment risk arising from single-classification results.Finally,generative adversarial network(GAN)is utilized to integrate multi-boundary fea-tures and confidence levels,generating a high-dimensional security boundary that reflects the overall security margin.The results of a case study verify that the proposed method can realize the online rapid generation of high-dimensional security boundaries and improve the credibility and interpretability of the model,providing support for the advanced op-eration functions of power grids such as security assessment,monitoring and early-warning.

关键词

热稳定安全域/基于密度的带噪声应用空间聚类/安全边界/改进自适应提升算法/生成式对抗网络

Key words

thermal security region(THSR)/density based spatial clustering of applications with noise(DBSCAN)/security boundary/AdaBoost.M2/generative adversarial network(GAN)

分类

信息技术与安全科学

引用本文复制引用

刘锡凯,薛文雅,曾沅,任郡枝,董向明,李良浩..融合DBSCAN与GAN集成学习的电力系统安全评估与边界生成方法[J].电力系统及其自动化学报,2026,38(6):46-55,10.

基金项目

国家电网有限公司科技项目(5100-202404010A-1-1-ZN). (5100-202404010A-1-1-ZN)

电力系统及其自动化学报

1003-8930

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