LAPSE:2023.26992
Published Article
LAPSE:2023.26992
Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids
April 3, 2023
The installation of smart meters at customer premises provides opportunities for the monitoring of distribution grids. This paper addresses the problem of improving the observability of low-voltage distribution grids using smart metering infrastructure. In particular, this paper deals with the application of state estimation algorithm using smart meter measurements for near-real-time monitoring of low-voltage distribution grids. This application is proposed to use a nonlinear weighted least squares method-based algorithm for estimating the node voltages from minimum number of smart meter measurements. This paper mainly deals with sensitivity analysis of the state estimation algorithm with respect to multiple uncertainties for, e.g., measurements errors, line parameter errors, and pseudo-measurements. Simulation studies are conducted to estimate the accuracy of the DSSE under various operating scenarios of a real-life low-voltage grid, and cost-effective ways to improve the accuracy of the state estimation algorithm are also evaluated. The paper concludes that by using smart meter measurements from few locations, voltage profiles of the low-voltage grid can be estimated with reasonable accuracy in near-real-time.
Keywords
distribution system state estimation, grid observability, sensitivity analysis, smart meters, weighted least squares method
Suggested Citation
Nainar K, Iov F. Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids. (2023). LAPSE:2023.26992
Author Affiliations
Nainar K: Department of Energy Technology, Aalborg Univerisity, 9220 Aalborg, Denmark [ORCID]
Iov F: Department of Energy Technology, Aalborg Univerisity, 9220 Aalborg, Denmark [ORCID]
Journal Name
Energies
Volume
13
Issue
20
Article Number
E5367
Year
2020
Publication Date
2020-10-15
Published Version
ISSN
1996-1073
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PII: en13205367, Publication Type: Journal Article
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LAPSE:2023.26992
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doi:10.3390/en13205367
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Apr 3, 2023
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