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 p...

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Autores principales:	Peyman Razmi, Mahdi Ghaemi Asl, Giorgio Canarella, Afsaneh Sadat Emami
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ff1b27c7cd349988cff8cd25a71b85e
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spelling	oai:doaj.org-article:5ff1b27c7cd349988cff8cd25a71b85e2021-12-02T20:11:09ZTopology identification in distribution system via machine learning algorithms.1932-620310.1371/journal.pone.0252436https://doaj.org/article/5ff1b27c7cd349988cff8cd25a71b85e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252436https://doaj.org/toc/1932-6203This 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.Peyman RazmiMahdi Ghaemi AslGiorgio CanarellaAfsaneh Sadat EmamiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252436 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peyman Razmi Mahdi Ghaemi Asl Giorgio Canarella Afsaneh Sadat Emami Topology identification in distribution system via machine learning algorithms.
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	article
author	Peyman Razmi Mahdi Ghaemi Asl Giorgio Canarella Afsaneh Sadat Emami
author_facet	Peyman Razmi Mahdi Ghaemi Asl Giorgio Canarella Afsaneh Sadat Emami
author_sort	Peyman Razmi
title	Topology identification in distribution system via machine learning algorithms.
title_short	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_sort	topology identification in distribution system via machine learning algorithms.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5ff1b27c7cd349988cff8cd25a71b85e
work_keys_str_mv	AT peymanrazmi topologyidentificationindistributionsystemviamachinelearningalgorithms AT mahdighaemiasl topologyidentificationindistributionsystemviamachinelearningalgorithms AT giorgiocanarella topologyidentificationindistributionsystemviamachinelearningalgorithms AT afsanehsadatemami topologyidentificationindistributionsystemviamachinelearningalgorithms
_version_	1718374948414685184

Topology identification in distribution system via machine learning algorithms.

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