Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement

TL;DR

This paper introduces a physics-informed heterogeneous graph neural network (PIHGNN) that efficiently determines optimal placement of dc-blockers in power grids to mitigate geomagnetic disturbance impacts, combining physical laws with graph learning.

Contribution

The paper presents a novel PIHGNN model that integrates physics-informed neural networks with heterogeneous graph neural networks for dc-blocker placement in power grids, addressing computational challenges.

Findings

PIHGNN effectively supports dc-blocker placement decisions.

The approach reduces computational costs compared to traditional methods.

Case studies validate the model's accuracy and efficiency.

Abstract

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.