Physics-Aware Neural Networks for Distribution System State Estimation

Understand

The distribution system state estimation problem seeks to determine the network state from available measurements.

• Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation.
• Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage.
• Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model.