This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and a vanilla neural network. Results show higher predictive accuracy than the vanilla network using the same training data and substantially reduced runtime compared with numerical solvers.
Reliable fault recovery of grid-forming (GFM) converters under current-limited conditions is increasingly important as inverter-based resources replace synchronous generation. Existing current-limiting strategies primarily focus on current-angle regulation and synchronization tra…
The whole-system impedance model has proven a powerful tool for assessing the small-signal stability of multi-inverter power systems; however, its application is limited to a small range around a steady-state operating point due to the inherent assumptions of time invariance and…
Large-scale power networks are often organized by geography, ownership, or control authority, making stability certificates that require a fully assembled global model challenging. This paper develops a time-domain small-signal stability certification framework for grid-forming i…
This work presents an examination of the main interactions between grid-following (GFL) and grid-forming (GFM) voltage source converters (VSCs) and synchronous generators (SGs), capturing the dynamics of a real power grid and pointing out the limitations of considering an ideal o…
As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW. Current projections estimate that approximately 50 GW of AI data center capaci…
This paper contributes to vehicle dynamics modeling by introducing a physics-informed neural state-space model tailored for the parking regime of a production battery-electric sedan, identified entirely from field-test maneuvers. At parking speeds the model captures what the kine…