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Topology identification in distribution system via machine learning algorithms
This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices’ status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propos...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168899/ https://www.ncbi.nlm.nih.gov/pubmed/34061910 http://dx.doi.org/10.1371/journal.pone.0252436 |
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author | Razmi, Peyman Ghaemi Asl, Mahdi Canarella, Giorgio Emami, Afsaneh Sadat |
author_facet | Razmi, Peyman Ghaemi Asl, Mahdi Canarella, Giorgio Emami, Afsaneh Sadat |
author_sort | Razmi, Peyman |
collection | PubMed |
description | This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices’ status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propose an approach to topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder’s voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning algorithms can track the voltage profile’s behavior in each feeder, detect the status of switching devices, identify the distribution system’s typologies, reveal the kind of loads connected or disconnected in the system, and estimate their values. Results are demonstrated under the implementation of the ANSI case study. |
format | Online Article Text |
id | pubmed-8168899 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81688992021-06-11 Topology identification in distribution system via machine learning algorithms Razmi, Peyman Ghaemi Asl, Mahdi Canarella, Giorgio Emami, Afsaneh Sadat PLoS One Research Article This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices’ status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propose an approach to topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder’s voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning algorithms can track the voltage profile’s behavior in each feeder, detect the status of switching devices, identify the distribution system’s typologies, reveal the kind of loads connected or disconnected in the system, and estimate their values. Results are demonstrated under the implementation of the ANSI case study. Public Library of Science 2021-06-01 /pmc/articles/PMC8168899/ /pubmed/34061910 http://dx.doi.org/10.1371/journal.pone.0252436 Text en © 2021 Razmi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Razmi, Peyman Ghaemi Asl, Mahdi Canarella, Giorgio Emami, Afsaneh Sadat Topology identification in distribution system via machine learning algorithms |
title | Topology identification in distribution system via machine learning algorithms |
title_full | Topology identification in distribution system via machine learning algorithms |
title_fullStr | Topology identification in distribution system via machine learning algorithms |
title_full_unstemmed | Topology identification in distribution system via machine learning algorithms |
title_short | Topology identification in distribution system via machine learning algorithms |
title_sort | topology identification in distribution system via machine learning algorithms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168899/ https://www.ncbi.nlm.nih.gov/pubmed/34061910 http://dx.doi.org/10.1371/journal.pone.0252436 |
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