Brint, A. orcid.org/0000-0002-8863-407X, Poursharif, G., Black, M. et al. (1 more author) (2018) Using grouped smart meter data in phase identification. Computers and Operations Research, 96. pp. 213-222. ISSN 0305-0548
Abstract
Access to smart meter data will enable electricity distribution companies to have a far clearer picture of the operation of their low voltage networks. This in turn will assist in the more active management of these networks. An important current knowledge gap is knowing for certain which phase each customer is connected to. Matching the loads from the smart meter with the loads measured on different phases at the substation has the capability to fill this gap. However, in the United Kingdom at the half hourly level only the loads from groups of meters will be available to the network operators. Therefore, a method is described for using this grouped data to assist with determining each customer's phase when the phase of most meters is correctly known. The method is analysed using the load readings from a data set of 96 smart meters. It successfully ranks the mixed phase groups very highly compared with the single phase groups.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier. This is an author produced version of a paper subsequently published in Computers & Operations Research. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Low voltage; Phasing; Smart meters; Ranking |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Mar 2018 12:52 |
Last Modified: | 09 Oct 2020 13:21 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.cor.2018.02.010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128194 |
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Filename: Phase_Identification_31_December_2017.pdf
Licence: CC-BY-NC-ND 4.0