基于混合量测时空动态异构的状态估计抗攻击功能设计及安全检测方法OA

With the deepening cyber-physical integration of new power systems,cyberattacks such as false data injection attacks(FDIAs)pose a significant threat to the functional security of state estimation.Existing detection approaches often rely heavily on attack-model priors,exhibit limited adaptability to stealthy or evolving threats,and are weakly integrated with core estimation modules.This paper considers hybrid measurement-based state estimation integrating supervisory control and data acquisition(SCADA)and wide-area measurement systems(WAMS),and proposes an attack-resilient functional design with embedded security detection based on spatiotemporal dynamic heterogeneity.A spatiotemporal disturbance mechanism is introduced to actively construct time-sampling and spatial-selection matrices,thereby injecting dynamic heterogeneity and structural uncertainty into the estimation process to invalidate the attacker's prior assumptions.Building on statistical characterization and significance testing,this study develops a mean residual shift(MRS)-based detection index that is tightly coupled with the estimator.A robust optimization-based detection model is developed to enable coordinated adjustment of disturbance strategies and detection thresholds,which is solved via a disturbance-response adversarial optimization(DRAO)algorithm.Simulations on standard test systems demonstrate that the proposed method effectively detects a wide range of enhanced FDIA strategies while preserving the accuracy of the underlying state estimation.