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

TL;DR

This paper introduces a combined matrix completion and compressed sensing method for state estimation in low-observable distribution systems, leveraging sparsity and grid constraints to improve accuracy with limited measurements.

Contribution

It proposes a novel approach that integrates low-rank matrix completion and compressive sensing, exploiting grid constraints for enhanced state estimation under low measurement availability.

Findings

Outperforms traditional matrix completion methods.

Achieves very low estimation errors at high compression ratios.

Validated on IEEE 37 test system with promising results.

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.