Communication Network-Aware Missing Data Recovery for Enhanced Distribution Grid Visibility

TL;DR

This paper introduces a communication-aware data recovery framework for distribution grids that considers routing constraints to improve accuracy when communication links fail.

Contribution

It integrates routing constraints with low-rank matrix completion and designs sensor clusters to prevent complete data loss during communication failures.

Findings

Significantly improves recovery accuracy over traditional methods.

Effective in handling spatially correlated measurement losses.

Validated on IEEE test feeders with real-world data.

Abstract

Power distribution systems increasingly rely on dense sensor networks for real-time monitoring, yet unreliable communication links and equipment malfunctions often result in missing or incomplete measurement sets at the operating center, requiring accurate data recovery techniques. Most existing approaches operate solely on the available measurements and overlook the role of the communication network that delivers sensor data, leading to large, spatially correlated losses when multiple sensors share failing communication links. This paper proposes a communication-aware framework that integrates routing constraints with low-rank matrix completion to improve data recovery accuracy under communication failures. Sensors are grouped into balanced clusters, and routing paths are designed to limit intracluster sensors sharing a common communication path, preventing complete data loss within any cluster. The remaining measurements for each cluster are then recovered using an optimal singular value thresholding (OSVT) method. Simulation results on the IEEE standard test feeder with real-world data demonstrate that the proposed framework significantly improves recovery accuracy compared to communication-agnostic, measurement-only methods.