  A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to
accurately, efficiently and robustly predict the nonlinear transient dynamics
of microgrids in the presence of disturbances. The graph-based architecture of
PIDGeuN provides a natural representation of the microgrid topology. Using only
the state information that is practically measurable, PIDGeuN employs a time
delay embedding formulation to fully reproduce the system dynamics, avoiding
the dependency of conventional methods on internal dynamic states such as
controllers. Based on a judiciously designed message passing mechanism, the
PIDGeuN incorporates two physics-informed techniques to improve its prediction
performance, including a physics-data-infusion approach to determining the
inter-dependencies between buses, and a loss term to respect the known physical
law of the power system, i.e., the Kirchhoff's law, to ensure the feasibility
of the model prediction. Extensive tests show that PIDGeuN can provide accurate
and robust prediction of transient dynamics for nonlinear microgrids over a
long-term time period. Therefore, the PIDGeuN offers a potent tool for the
modeling of large scale networked microgrids (NMs), with potential applications
to predictive or preventive control in real time applications for the stable
and resilient operations of NMs.
