Tractable learning in under-excited power grids

• URL: http://arxiv.org/abs/2005.01818v1
• Date: Mon, 4 May 2020 19:54:48 GMT
• Title: Tractable learning in under-excited power grids
• Authors: Deepjyoti Deka, Harish Doddi, Sidhant Misra, Murti Salapaka
• Abstract summary: We propose a novel learning algorithm for learning underexcited general (non-radial) networks based on physics-informed conservation laws. We prove the correctness of our algorithm for grids with non-adjacent under-excited internal nodes. Our approach is validated through simulations with non-linear voltage samples generated on test grids with real injection data.
• Score: 4.568911586155097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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