Poursharif, G., Brint, A. orcid.org/0000-0002-8863-407X, Holliday, J. et al. (2 more authors) (2018) Low voltage current estimation using AMI/smart meter data. International Journal of Electrical Power and Energy Systems, 99. pp. 290-298. ISSN 0142-0615
Abstract
Knowledge of the currents is a key foundation for smart grid applications. However, knowledge of low voltage currents is generally poor. The new information streams from advanced metering infrastructure (AMI)/smart meters and the monitoring of distribution substations offer the opportunity of rectifying this. Unfortunately, often not all the smart meter readings will be available in real-time. For example, this situation will arise when older (non-compliant) smart meters do not have real-time reporting capabilities. This paper investigates how knowledge of the substation currents can be combined with the available real-time AMI/smart meter readings and the historical readings from the non-real-time meters, to estimate these missing values. It is found that the k-nearest neighbor weighted average approach performs best but that the gains over using simpler methods are relatively modest.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier Ltd. This is an author produced version of a paper subsequently published in International Journal of Electrical Power & Energy Systems. 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/) |
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: | 20 Feb 2018 11:57 |
Last Modified: | 20 Feb 2019 01:38 |
Published Version: | https://doi.org/10.1016/j.ijepes.2018.01.023 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.ijepes.2018.01.023 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127717 |