A Physics‐Informed Graph‐Masked Autoencoder for Robust Joint Topology–State Estimation in Distribution Systems

Incomplete measurements, topology uncertainty, and abrupt reconfigurations significantly degrade situational awareness in modern electric distribution systems, especially under high renewable penetration. Conventional sequential ‘identify‐then‐estimate’ schemes suffer from severe error propagation and delayed responsiveness. To bridge this critical gap, this work proposes a physics‐informed graph‐masked autoencoder (PI‐GMAE) framework, integrating topology identification (TI) and state estimation (SE) into a unified, self‐supervised paradigm. Numerical validation confirms that PI‐GMAE achieves instantaneous topology tracking and accurate state recovery even under 50% measurement masking, effectively neutralising the error accumulation of sequential methods to support real‐time situational awareness in modern distribution systems.