Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation

This paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure at the remaining nodes using Kriging spatial inter...

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Autores: Soldevila, Adrià, Blesa, Joaquim, Fernández Cantí, Rosa M., Tornil-Sin, Sebastian, Puig, Vicenç
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/202137
Acceso en línea:http://hdl.handle.net/10261/202137
Access Level:acceso abierto
Palabra clave:Water distribution networks
Leak localization
Data-driven methods
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network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
title Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
spellingShingle Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
Soldevila, Adrià
Water distribution networks
Leak localization
Data-driven methods
title_short Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
title_full Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
title_fullStr Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
title_full_unstemmed Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
title_sort Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation
dc.creator.none.fl_str_mv Soldevila, Adrià
Blesa, Joaquim
Fernández Cantí, Rosa M.
Tornil-Sin, Sebastian
Puig, Vicenç
author Soldevila, Adrià
author_facet Soldevila, Adrià
Blesa, Joaquim
Fernández Cantí, Rosa M.
Tornil-Sin, Sebastian
Puig, Vicenç
author_role author
author2 Blesa, Joaquim
Fernández Cantí, Rosa M.
Tornil-Sin, Sebastian
Puig, Vicenç
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Agencia Estatal de Investigación (España)
European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Generalitat de Catalunya
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Water distribution networks
Leak localization
Data-driven methods
topic Water distribution networks
Leak localization
Data-driven methods
description This paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure at the remaining nodes using Kriging spatial interpolation. Online leak localization is attained by comparing current pressure values with their reference values. Supported by Kriging; this comparison can be performed for all the network nodes, not only for those equipped with pressure sensors. On the one hand, reference pressure values in all nodes are obtained by applying Kriging to measurement data previously recorded under network operation without leaks. On the other hand, current pressure values at all nodes are obtained by applying Kriging to the current measured pressure values. The node that presents the maximum difference (residual) between current and reference pressure values is proposed as a leaky node candidate. Thereafter, a time horizon computation based on Bayesian reasoning is applied to consider the residual time evolution, resulting in an improved leak localization accuracy. As a data-driven approach, the proposed method does not need a hydraulic model; only historical data from normal operation is required. This is an advantage with respect to most data-driven methods that need historical data for the considered leak scenarios. Since, in practice, the obtained leak localization results will strongly depend on the number of available pressure measurements and their location, an optimal sensor placement procedure is also proposed in the paper. Three different case studies illustrate the performance of the proposed methodologies.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/202137
url http://hdl.handle.net/10261/202137
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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DPI2017-88403-R/AEI/10.13039/501100011033
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dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
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spelling Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolationSoldevila, AdriàBlesa, JoaquimFernández Cantí, Rosa M.Tornil-Sin, SebastianPuig, VicençWater distribution networksLeak localizationData-driven methodsThis paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure at the remaining nodes using Kriging spatial interpolation. Online leak localization is attained by comparing current pressure values with their reference values. Supported by Kriging; this comparison can be performed for all the network nodes, not only for those equipped with pressure sensors. On the one hand, reference pressure values in all nodes are obtained by applying Kriging to measurement data previously recorded under network operation without leaks. On the other hand, current pressure values at all nodes are obtained by applying Kriging to the current measured pressure values. The node that presents the maximum difference (residual) between current and reference pressure values is proposed as a leaky node candidate. Thereafter, a time horizon computation based on Bayesian reasoning is applied to consider the residual time evolution, resulting in an improved leak localization accuracy. As a data-driven approach, the proposed method does not need a hydraulic model; only historical data from normal operation is required. This is an advantage with respect to most data-driven methods that need historical data for the considered leak scenarios. Since, in practice, the obtained leak localization results will strongly depend on the number of available pressure measurements and their location, an optimal sensor placement procedure is also proposed in the paper. Three different case studies illustrate the performance of the proposed methodologies.This work has been funded by the Ministerio de Economía, Industria y Competitividad (MEICOMP) of the Spanish Government and FEDER through the project DEOCS (ref. DPI2016-76493) and SCAV (ref. DPI2017-88403-R), by MEICOMP and FEDER through the grant IJCI-2014-20801, and by the Catalan Agency for Management of University and Research Grants (AGAUR), the European Social Fund (ESF) and the Secretary of University and Research of the Department of Companies and Knowledge of the Government of Catalonia through the grant FI-DGR 2015 (ref. 2015 FI B 00591) and the Advanced Control Systems (SAC) group grant (2017 SGR 482). The second author acknowledges the support from the Serra Húnter program.Molecular Diversity Preservation InternationalMinisterio de Economía y Competitividad (España)Agencia Estatal de Investigación (España)European CommissionMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Generalitat de CatalunyaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020192020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/202137reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493DPI2017-88403-R/AEI/10.13039/501100011033info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-88403-RDPI2017-88403-R/AEI/10.13039/501100011033info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI-2014-20801http://dx.doi.org/10.3390/w11071500Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2021372026-05-22T06:33:51Z
score 15,81155

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