Adaptive Data Fusion for State Estimation and Control of Power Grids Under Attack

Current power system state estimation and control methods are susceptible to false data injection attacks (FDIAs), which introduce faulty measurements throughout the grid that decrease grid stability. Fusing sensor measurements can reduce errors in state estimation, and data-driven approaches have been increasingly used for defense against FDIAs. However, current methods often lack adaptability and focus only on detection while failing to address attack effects on estimation and control. This work proposes AstroFusion, an adaptive data fusion framework that makes power grid state estimation and control more resilient to attacks. AstroFusion employs deep multilayer perceptrons to identify which sensors may be under attack and adaptively selects from an ensemble of data-driven models to improve the state estimation. This work is the first to characterize the performance of autonomous power grid controllers in the presence of varying attacks. Results are shown on IEEE 14-bus, IEEE 36-bus, and IEEE 118-bus systems.