  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.
