Adam Mate

John Slane, Adam Mate

In recent years, the contribution of renewable energy resources to the electrical grid has increased drastically; the most common of these are photovoltaic solar panels and wind turbines. These resources rely on inverters to interface with the grid, which do not inherently exhibit the same fault characteristics as synchronous generators. Consistently, they can strain grid reliability and security, cause increased number of blackouts, and, in some cases, allow relatively minor faults to turn into cascading failures. Solar and wind energy provide benefits and can support grid stability; however, several challenges and gaps in understanding must be explored and addressed before this can be realized. This paper provides a comprehensive literature review of grid codes, modeling techniques, and tools, as well as current methods for responding to various faults. It also presents an overview of the industry's state as it relates to grid fault response in the presence of inverter-based resources.

Hongwei Jin, Prasanna Balaprakash, Allen Zou et al.

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.