基于数据-机理双驱动的电网潮流信息推断与防御方法OA

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.