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Limited measurement availability at the distribution grid presents challengesfor state estimation and situational awareness. This paper combines theadvantages of two sparsity-based state estimation approaches (matrix completionand compressive sensing) that have been proposed recently to address thechallenge of unobservability. The proposed approach exploits both the low rankstructure and a suitable transform domain representation to leverage thecorrelation structure of the spatio-temporal data matrix while incorporatingthe power-flow constraints of the distribution grid. Simulations are carriedout on three phase unbalanced IEEE 37 test system to verify the effectivenessof the proposed approach. The performance results reveal - (1) the superiorityover traditional matrix completion and (2) very low state estimation errors forhigh compression ratios representing very low observability.

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