LAPSE:2023.7410
Published Article

LAPSE:2023.7410
Data Analytics for Admittance Matrix Estimation of Poorly Monitored Distribution Grids
February 24, 2023
Abstract
Smart grid operations require accurate information on network topology and electrical equipment parameters. This paper proposes estimating such information with data from the smart grid. Assuming that the availability of bus voltage data is restricted to their magnitude, a linear model of the relationship between these data and the parameters of the admittance matrix is derived in a way that does not involve bus voltage angles. A regression optimizer is then proposed to minimize the deviation between data and values estimated by the linear model. Results on the IEEE 33 bus system are presented to illustrate the model accuracy and efficiency when used to estimate parameters of medium-voltage, three-phase balanced grids.
Smart grid operations require accurate information on network topology and electrical equipment parameters. This paper proposes estimating such information with data from the smart grid. Assuming that the availability of bus voltage data is restricted to their magnitude, a linear model of the relationship between these data and the parameters of the admittance matrix is derived in a way that does not involve bus voltage angles. A regression optimizer is then proposed to minimize the deviation between data and values estimated by the linear model. Results on the IEEE 33 bus system are presented to illustrate the model accuracy and efficiency when used to estimate parameters of medium-voltage, three-phase balanced grids.
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Keywords
admittance matrix, data-driven, inverse power flow, smart grids, smart meter
Subject
Suggested Citation
Leal PC, Ferreira DMVP, Carvalho PMS. Data Analytics for Admittance Matrix Estimation of Poorly Monitored Distribution Grids. (2023). LAPSE:2023.7410
Author Affiliations
Leal PC: Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; INESC-ID, 1000-029 Lisbon, Portugal [ORCID]
Ferreira DMVP: Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; INESC-ID, 1000-029 Lisbon, Portugal [ORCID]
Carvalho PMS: Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; INESC-ID, 1000-029 Lisbon, Portugal [ORCID]
Ferreira DMVP: Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; INESC-ID, 1000-029 Lisbon, Portugal [ORCID]
Carvalho PMS: Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; INESC-ID, 1000-029 Lisbon, Portugal [ORCID]
Journal Name
Energies
Volume
15
Issue
23
First Page
8961
Year
2022
Publication Date
2022-11-27
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15238961, Publication Type: Journal Article
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LAPSE:2023.7410
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https://doi.org/10.3390/en15238961
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[v1] (Original Submission)
Feb 24, 2023
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Feb 24, 2023
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