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:
-
- 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}
}
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