Graph Neural Ordinary Differential Equations for Power System Identification with Transfer Learning Applications
The MPG-NODE framework proposes a novel control-oriented message passing graph neural ordinary differential equation for power system identification. It incorporates learned embeddings for heterogeneous node dynamics and edge couplings, allowing it to model complex power systems with varying components. The method shows good performance and offers significant flexibility through transfer learning, enabling easy adaptation to changing system topologies.