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

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. Here, 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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Iowa State Univ., Ames, IA (United States)
Publication Date:
Research Org.:
Iowa State Univ., Ames, IA (United States)
Sponsoring Org.:
USDOE Office of Electricity (OE); National Science Foundation (NSF)
OSTI Identifier:
1961208
Grant/Contract Number:  
OE0000875; EPCN 2042314
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: 12; Journal Issue: 5; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; Smart meters; sensor asynchrony; low rank matrix recovery; multi-objective optimization

Citation Formats

Yuan, Yuxuan, Dehghanpour, Kaveh, and Wang, Zhaoyu. Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery. United States: N. p., 2021. Web. doi:10.1109/tsg.2021.3088835.
Yuan, Yuxuan, Dehghanpour, Kaveh, & Wang, Zhaoyu. Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery. United States. https://doi.org/10.1109/tsg.2021.3088835
Yuan, Yuxuan, Dehghanpour, Kaveh, and Wang, Zhaoyu. 2021. "Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery". United States. https://doi.org/10.1109/tsg.2021.3088835. https://www.osti.gov/servlets/purl/1961208.
@article{osti_1961208,
title = {Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery},
author = {Yuan, Yuxuan and Dehghanpour, Kaveh and Wang, Zhaoyu},
abstractNote = {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. Here, 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.},
doi = {10.1109/tsg.2021.3088835},
url = {https://www.osti.gov/biblio/1961208}, journal = {IEEE Transactions on Smart Grid},
issn = {1949-3053},
number = 5,
volume = 12,
place = {United States},
year = {Mon Jun 14 00:00:00 EDT 2021},
month = {Mon Jun 14 00:00:00 EDT 2021}
}

Works referenced in this record:

Uncertainty analysis of aggregated smart meter data for state estimation
conference, September 2016


Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance
journal, May 2014


Fast principal component pursuit via alternating minimization
conference, September 2013


Stable Principal Component Pursuit
conference, June 2010


Optimal capacitor placement on radial distribution systems
journal, January 1989


Optimal Distributed Feedback Voltage Control Under Limited Reactive Power
journal, January 2020


Smooth minimization of non-smooth functions
journal, December 2004


A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
journal, March 2019


A Review on Distribution System State Estimation
journal, September 2017


Impact of time interval alignment on data quality in electricity grids
conference, October 2018


Approximation of the Time Alignment Error for Measurements in Electricity Grids
conference, September 2019


Dynamic Power Network State Estimation with Asynchronous Measurements
conference, November 2019


The Credibility Modelling and Analysis of AMI Measurements for Distribution System State Estimation
conference, November 2019


Distribution System State Estimation Based on Nonsynchronized Smart Meters
journal, November 2015


A review of multi-objective optimization: Methods and its applications
journal, January 2018


A Revised Branch Current-Based Distribution System State Estimation Algorithm and Meter Placement Impact
journal, February 2004


A branch-current-based state estimation method for distribution systems
journal, January 1995


A Multi-Timescale Data-Driven Approach to Enhance Distribution System Observability
journal, July 2019


A Time-Series Distribution Test System Based on Real Utility Data
conference, October 2019


Measurement Placement in Distribution System State Estimation
journal, May 2009