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Network identification using micro-PMU and smart meter measurements
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Shah, Priyank and Zhao, Xiaowei (2022) Network identification using micro-PMU and smart meter measurements. IEEE Transactions on Industrial Informatics, 18 (11). pp. 7572-7586. doi:10.1109/TII.2022.3156652 ISSN 1551-3203.
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WRAP-network-identification-using-micro-PMU-smart-meter-measurements-Zhao-2022.pdf - Accepted Version - Requires a PDF viewer. Download (1840Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TII.2022.3156652
Abstract
The network identification plays a very prominent role for the network operator to accomplish the various objectives such as state-estimation, monitoring, control, planning, and real-time analytics. The network structure varies from time-to-time and its details are often not available with the network operator. To address this issue, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter (SM) and micro phasor measurement unit (micro-PMU) measurements. The presented algorithm is divided into two sections, 1) approximate parameter evaluation through regression, to extract the partial topology information, and 2) complete network topology identification through the ADMM framework. This algorithm accomplishes the objectives of identifying the network configuration, branch parameters (e.g. conductance and susceptance), and change in branch parameters. Simulation results demonstrate the effectiveness of the presented algorithm on the benchmarked IEEE 13-bus and IEEE 123-bus feeders under various operating scenarios. Furthermore, the presented framework illustrates excellent network identification even with the presence of the stochastic nature of renewable power generation. The presented algorithm exhibits an excellent performance even with the consideration of noise in both measurements. In addition, the comparative performance is carried out on the benchmarked unbalanced IEEE 13-bus and balanced IEEE 33-bus feeders to highlight the efficacy of the presented framework over the state-of-art framework.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Parameter estimation , Electric power systems -- Measurement, Smart power grids, Electric meters, Intelligent control system | ||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1551-3203 | ||||||||
Official Date: | November 2022 | ||||||||
Dates: |
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Volume: | 18 | ||||||||
Number: | 11 | ||||||||
Page Range: | pp. 7572-7586 | ||||||||
DOI: | 10.1109/TII.2022.3156652 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | IEEE | ||||||||
Date of first compliant deposit: | 26 May 2022 | ||||||||
Date of first compliant Open Access: | 26 May 2022 | ||||||||
RIOXX Funder/Project Grant: |
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