中国电机工程学报2026，Vol.46Issue(14)：5708-5719,中插2,13.DOI:10.13334/j.0258-8013.pcsee.250642
基于数据-机理双驱动的电网潮流信息推断与防御方法
Data-mechanism Hybrid Driven Power Flow Data Inference and Defense Methods
摘要
Abstract
With the accelerating digital transformation of power systems,the trend toward open data sharing has intensified the severity of data inference threats.This paper focuses on the security of power flow information,a key sensitive data in power systems.First,an inference model for power flow information is constructed,revealing the nonlinear relationship between public data and sensitive power flow data through a multi-level coupling relationship framework.Based on this,a data-mechanism hybrid-driven power flow information inference method is proposed,which informsphysical constraints of power systems into a deep graph learning approach to achieve accurate power flow information inference that conforms to power systems operation rules.Comparative experiments show that this method has superior inference performance compared to traditional neural network methods.The research further analyzes the influence mechanism of multi-source data synergy on information leakage risks,and systematically evaluates the protective effects of typical data protection techniques.An optimized defense strategy that balances data security and usability is proposed,providing methodological support for data security in power systems.关键词
潮流信息推断/数据-机理双驱动/威胁评估/防御方法/电力系统Key words
power flow inference/data-mechanism hybrid driven/threat assessment/defense strategy/power system分类
信息技术与安全科学引用本文复制引用
吴桐,冯江琳,杨雨洁,王子骏,刘杨,刘烃,管晓宏..基于数据-机理双驱动的电网潮流信息推断与防御方法[J].中国电机工程学报,2026,46(14):5708-5719,中插2,13.基金项目
国家重点研发计划项目(2022YFB2703500) （2022YFB2703500）
国家自然科学基金(重大项目)(62293500).National Key R&D Program of China(2022YFB2703500) （重大项目）
Project Supported by National Natural Science Foundation of China(Major Program)(62293500). （Major Program）
