Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery

TL;DR

This paper introduces a novel multi-objective low rank matrix recovery method using principal component pursuit to mitigate smart meter asynchrony errors, improving data quality and distribution network monitoring accuracy.

Contribution

It proposes a new PCP-based low rank matrix recovery approach with a multi-objective structure to enhance smart meter data synchronization and accuracy.

Findings

Effective in reducing asynchrony errors in real smart meter data

Improves accuracy of voltage and power measurements

Enhances distribution grid monitoring reliability

Abstract

Smart meters (SMs) are being widely deployed by distribution utilities across the U.S. Despite their benefits in real-time monitoring. SMs suffer from certain data quality issues; specifically, unlike phasor measurement units (PMUs) that use GPS for data synchronization, SMs are not perfectly synchronized. The asynchrony error can degrade the monitoring accuracy in distribution networks. To address this challenge, we propose a principal component pursuit (PCP)-based data recovery strategy. Since asynchrony results in a loss of temporal correlation among SMs, the key idea in our solution is to leverage a PCP-based low rank matrix recovery technique to maximize the temporal correlation between multiple data streams obtained from SMs. Further, our approach has a novel multi-objective structure, which allows utilities to precisely refine and recover all SM-measured variables, including voltage and power measurements, while incorporating their inherent dependencies through power flow equations. We have performed numerical experiments using real SM data to demonstrate the effectiveness of the proposed strategy in mitigating the impact of SM asynchrony on distribution grid monitoring.