A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform

A comprehensive knowledge of topology is of great importance for the effective operation and maintenance of distribution networks. This paper contributes with a novel data-driven topology identification method for low-voltage distribution networks based on the wavelet transform. The method uses only...

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Autores: García Caro, Sebastián, Fresia, Matteo, Mora-Merchán, Javier María, Carrasco Muñoz, Alejandro, Personal Vázquez, Enrique, León de Mora, Carlos
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/174143
Acceso en línea:https://hdl.handle.net/11441/174143
https://doi.org/10.1016/j.epsr.2025.111517
Access Level:acceso abierto
Palabra clave:Distribution networks
Smart meters
Data analytics
Topology Identification
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spelling A data-driven topology identification method for low-voltage distribution networks based on the wavelet transformGarcía Caro, SebastiánFresia, MatteoMora-Merchán, Javier MaríaCarrasco Muñoz, AlejandroPersonal Vázquez, EnriqueLeón de Mora, CarlosDistribution networksSmart metersData analyticsTopology IdentificationA comprehensive knowledge of topology is of great importance for the effective operation and maintenance of distribution networks. This paper contributes with a novel data-driven topology identification method for low-voltage distribution networks based on the wavelet transform. The method uses only energy measurements from smart meters, being compatible with the current European smart meter capabilities. The method identifies the feeder and phase topology of single and three-phase customers, even in unbalanced situations. A computationally-efficient methodology to link customers' time-frequency features with their network connection is proposed. The performance of the method is assessed on eleven non-synthetic networks, with a robustness assessment of factors such as network observability, dataset size, measurement errors, and Renewable Energy Sources (RES) penetration. Accuracy rates exceeding 95 % are obtained in most cases, outperforming an energy-conservation approach. A 98 % accuracy can be achieved with a 30-day hourly dataset if at least 80 % of network observability is provided. For lower observability levels, 45 or 60 days of data are needed to reach similar rates. The sensitivity analysis of measurement error demonstrated that it had a negligible influence on the results. The method showed favorable results even in scenarios with high-RES penetration, with accuracy values exceeding 95 %.ElsevierTecnología ElectrónicaTIC150: Tecnología Electrónica e Informática IndustrialMinisterio de Ciencia, Innovación y Universidades (MICIU). EspañaEuropean Commission. Fondo Social Europeo (FSO)2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/174143https://doi.org/10.1016/j.epsr.2025.111517reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésElectric Power Systems Research, 243, 111517.TED2021–129702B-I00https://www.sciencedirect.com/science/article/pii/S0378779625001099info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1741432026-06-17T12:51:07Z
dc.title.none.fl_str_mv A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
title A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
spellingShingle A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
García Caro, Sebastián
Distribution networks
Smart meters
Data analytics
Topology Identification
title_short A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
title_full A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
title_fullStr A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
title_full_unstemmed A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
title_sort A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
dc.creator.none.fl_str_mv García Caro, Sebastián
Fresia, Matteo
Mora-Merchán, Javier María
Carrasco Muñoz, Alejandro
Personal Vázquez, Enrique
León de Mora, Carlos
author García Caro, Sebastián
author_facet García Caro, Sebastián
Fresia, Matteo
Mora-Merchán, Javier María
Carrasco Muñoz, Alejandro
Personal Vázquez, Enrique
León de Mora, Carlos
author_role author
author2 Fresia, Matteo
Mora-Merchán, Javier María
Carrasco Muñoz, Alejandro
Personal Vázquez, Enrique
León de Mora, Carlos
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Tecnología Electrónica
TIC150: Tecnología Electrónica e Informática Industrial
Ministerio de Ciencia, Innovación y Universidades (MICIU). España
European Commission. Fondo Social Europeo (FSO)
dc.subject.none.fl_str_mv Distribution networks
Smart meters
Data analytics
Topology Identification
topic Distribution networks
Smart meters
Data analytics
Topology Identification
description A comprehensive knowledge of topology is of great importance for the effective operation and maintenance of distribution networks. This paper contributes with a novel data-driven topology identification method for low-voltage distribution networks based on the wavelet transform. The method uses only energy measurements from smart meters, being compatible with the current European smart meter capabilities. The method identifies the feeder and phase topology of single and three-phase customers, even in unbalanced situations. A computationally-efficient methodology to link customers' time-frequency features with their network connection is proposed. The performance of the method is assessed on eleven non-synthetic networks, with a robustness assessment of factors such as network observability, dataset size, measurement errors, and Renewable Energy Sources (RES) penetration. Accuracy rates exceeding 95 % are obtained in most cases, outperforming an energy-conservation approach. A 98 % accuracy can be achieved with a 30-day hourly dataset if at least 80 % of network observability is provided. For lower observability levels, 45 or 60 days of data are needed to reach similar rates. The sensitivity analysis of measurement error demonstrated that it had a negligible influence on the results. The method showed favorable results even in scenarios with high-RES penetration, with accuracy values exceeding 95 %.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/174143
https://doi.org/10.1016/j.epsr.2025.111517
url https://hdl.handle.net/11441/174143
https://doi.org/10.1016/j.epsr.2025.111517
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Electric Power Systems Research, 243, 111517.
TED2021–129702B-I00
https://www.sciencedirect.com/science/article/pii/S0378779625001099
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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