Advancing GridFM from Power Flow to State Estimation

To date, GridFM has demonstrated remarkable efficiency in Power Flow (PF) estimation, achieving orders-of-magnitude reductions in computational cost. In this breakout session, we will explore extending GridFM to State Estimation (SE). Leveraging a masked autoencoder approach holds promiss to accurately reconstruct missing grid topology and effectively denoise sensor data even under non-Gaussian noise.

We will outline a study to test this capability for both transmission and distribution grids, using realistic parameters under quasi-static and/or dynamic operation scenarios. The discussion will also cover: i) available datasets relevant for SE and ii) architectural enhancements to GridFM needed to handle the additional complexities of SE compared to PF estimation only.

By the end of the session, our goal is to have a clear and detailed plan for implementing a technical proof-of-concept for GridFM+, which excels in PF and SE.