Robust data-driven leak localization in water distribution networks using pressure measurements and topological information

This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selecte...

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Autores: Alves, Débora Cristina Costa da Silva|||0000-0003-3207-4189, Blesa Izquierdo, Joaquim|||0000-0002-5626-3753, Duviella, Eric, Rajaoarisoa, Lala H.
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/360483
Acceso en línea:https://hdl.handle.net/2117/360483
https://dx.doi.org/10.3390/s21227551
Access Level:acceso abierto
Palabra clave:Water -- Distribution
Water distribution networks
Leak localization
Data-driven
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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network_name_str España
repository_id_str
spelling Robust data-driven leak localization in water distribution networks using pressure measurements and topological informationAlves, Débora Cristina Costa da Silva|||0000-0003-3207-4189Blesa Izquierdo, Joaquim|||0000-0002-5626-3753Duviella, EricRajaoarisoa, Lala H.Water -- DistributionWater distribution networksLeak localizationData-drivenAigua -- DistribucióÀrees temàtiques de la UPC::Informàtica::Automàtica i controlThis article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.This work has been partially funded by SMART Project (ref.num. EFA153/16 Interreg Cooperation Program POCTEFA 2014-2020), L-BEST Project (PID2020-115905RB-C21) funded by MCIN/ AEI /10.13039/501100011033 and AGAUR ACCIO RIS3CAT UTILITIES 4.0–P1 ACTIV 4.0. ref.COMRDI-16-1-0054-03.Peer Reviewed20212021-11-1320222022-01-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/360483https://dx.doi.org/10.3390/s21227551reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-115905RB-C21 SUPERVISION Y CONTROL TOLERANTE A FALLOS DE INFRAESTRUCTURAS INTELIGENTES BASADO EN APRENDIZAJE AVANZADO Y OPTIMIZACIONopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3604832026-05-27T15:37:01Z
dc.title.none.fl_str_mv Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
title Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
spellingShingle Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
Alves, Débora Cristina Costa da Silva|||0000-0003-3207-4189
Water -- Distribution
Water distribution networks
Leak localization
Data-driven
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
title_full Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
title_fullStr Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
title_full_unstemmed Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
title_sort Robust data-driven leak localization in water distribution networks using pressure measurements and topological information
dc.creator.none.fl_str_mv Alves, Débora Cristina Costa da Silva|||0000-0003-3207-4189
Blesa Izquierdo, Joaquim|||0000-0002-5626-3753
Duviella, Eric
Rajaoarisoa, Lala H.
author Alves, Débora Cristina Costa da Silva|||0000-0003-3207-4189
author_facet Alves, Débora Cristina Costa da Silva|||0000-0003-3207-4189
Blesa Izquierdo, Joaquim|||0000-0002-5626-3753
Duviella, Eric
Rajaoarisoa, Lala H.
author_role author
author2 Blesa Izquierdo, Joaquim|||0000-0002-5626-3753
Duviella, Eric
Rajaoarisoa, Lala H.
author2_role author
author
author
dc.subject.none.fl_str_mv Water -- Distribution
Water distribution networks
Leak localization
Data-driven
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Water -- Distribution
Water distribution networks
Leak localization
Data-driven
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-11-13
2022
2022-01-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/360483
https://dx.doi.org/10.3390/s21227551
url https://hdl.handle.net/2117/360483
https://dx.doi.org/10.3390/s21227551
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-115905RB-C21 SUPERVISION Y CONTROL TOLERANTE A FALLOS DE INFRAESTRUCTURAS INTELIGENTES BASADO EN APRENDIZAJE AVANZADO Y OPTIMIZACION
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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