  The threat of geomagnetic disturbances (GMDs) to the reliable operation of
the bulk energy system has spurred the development of effective strategies for
mitigating their impacts. One such approach involves placing transformer
neutral blocking devices, which interrupt the path of geomagnetically induced
currents (GICs) to limit their impact. The high cost of these devices and the
sparsity of transformers that experience high GICs during GMD events, however,
calls for a sparse placement strategy that involves high computational cost. To
address this challenge, we developed a physics-informed heterogeneous graph
neural network (PIHGNN) for solving the graph-based dc-blocker placement
problem. Our approach combines a heterogeneous graph neural network (HGNN) with
a physics-informed neural network (PINN) to capture the diverse types of nodes
and edges in ac/dc networks and incorporates the physical laws of the power
grid. We train the PIHGNN model using a surrogate power flow model and validate
it using case studies. Results demonstrate that PIHGNN can effectively and
efficiently support the deployment of GIC dc-current blockers, ensuring the
continued supply of electricity to meet societal demands. Our approach has the
potential to contribute to the development of more reliable and resilient power
grids capable of withstanding the growing threat that GMDs pose.
