Matrix Completion for Low-Observability Voltage Estimation

TL;DR

This paper introduces a matrix completion-based state estimation method for distribution systems that performs well under low-observability conditions, leveraging power flow constraints to accurately estimate system states.

Contribution

It presents a novel low-rank matrix completion approach combined with noise-resilient power flow constraints for distribution system state estimation under low-observability.

Findings

Achieves near-perfect state estimation with less than 1% error.

Performs effectively across various low-observability scenarios.

Validated on IEEE test systems with strong results.

Abstract

With the rising penetration of distributed energy resources, distribution system control and enabling techniques such as state estimation have become essential to distribution system operation. However, traditional state estimation techniques have difficulty coping with the low-observability conditions often present on the distribution system due to the paucity of sensors and heterogeneity of measurements. To address these limitations, we propose a distribution system state estimation algorithm that employs matrix completion (a tool for estimating missing values in low-rank matrices) augmented with noise-resilient power flow constraints. This method operates under low-observability conditions where standard least-squares-based methods cannot operate, and flexibly incorporates any network quantities measured in the field. We empirically evaluate our method on the IEEE 33- and 123-bus test systems, and find that it provides near-perfect state estimation performance (within 1\% mean absolute percent error) across many low-observability data availability regimes.