Tractable learning in under-excited power grids
This addresses a critical issue for power grid security by enabling structure estimation in scenarios where prior methods fail, though it is incremental as it builds on existing physics-based approaches.
The paper tackles the problem of estimating power grid structure in the under-excited regime where some nodes lack external injection, by proposing a novel topology learning algorithm based on physics-informed conservation laws, and proves its asymptotic correctness with theoretical noise bounds and validation through simulations.
Estimating the structure of physical flow networks such as power grids is critical to secure delivery of energy. This paper discusses statistical structure estimation in power grids in the "under-excited" regime, where a subset of internal nodes do not have external injection. Prior estimation algorithms based on nodal potentials or voltages fail in the under-excited regime. We propose a novel topology learning algorithm for learning underexcited general (non-radial) networks based on physics-informed conservation laws. We prove the asymptotic correctness of our algorithm for grids with non-adjacent under-excited internal nodes. More importantly, we theoretically analyze our algorithm's efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with non-linear voltage samples generated on test grids with real injection data