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arXiv · 2104.06470

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Shweta Dahale·Balasubramaniam Natarajan
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Abstract

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the power-flow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

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Shweta Dahale, Balasubramaniam Natarajan. 2021-04-13. Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System. https://arxiv.org/abs/2104.06470

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