Extending Grid Foundation Models for Phasor-Domain Dynamic Simulation

arXiv cs.SY2026-11-09 13:000 views

arXiv:2611.08867v1 Announce Type: new Abstract: Grid Foundation Models (GridFMs) are emerging as a powerful framework for learning and transferring knowledge across power grids and grid analysis tasks. This work extends GridFMs from steady-state analysis to dynamic simulation. We present DynGridFM, which uses component-wise tokenization and a structured latent operator to learn shared dynamic representations across heterogeneous grids and enable transfer to unseen systems. Pretrained on nine grids, DynGridFM is adapted to a held-out 48-machine grid, where fine-tuning on 64 trajectories achieves accuracy comparable to training from scratch on 128. On a single GPU, it achieves 924x to 1232x speedup over numerical simulators on one CPU core.

Source：https://arxiv.org/abs/2611.08867

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Extending Grid Foundation Models for Phasor-Domain Dynamic Simulation

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