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 principales:	Débora Alves, Joaquim Blesa, Eric Duviella, Lala Rajaoarisoa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	water distribution networks leak localization data-driven Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/327d213316c047bba49151e123849aaa
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spelling	oai:doaj.org-article:327d213316c047bba49151e123849aaa2021-11-25T18:57:23ZRobust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information10.3390/s212275511424-8220https://doaj.org/article/327d213316c047bba49151e123849aaa2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7551https://doaj.org/toc/1424-8220This 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 <i>k</i> 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.Débora AlvesJoaquim BlesaEric DuviellaLala RajaoarisoaMDPI AGarticlewater distribution networksleak localizationdata-drivenChemical technologyTP1-1185ENSensors, Vol 21, Iss 7551, p 7551 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	water distribution networks leak localization data-driven Chemical technology TP1-1185
spellingShingle	water distribution networks leak localization data-driven Chemical technology TP1-1185 Débora Alves Joaquim Blesa Eric Duviella Lala Rajaoarisoa Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information
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 <i>k</i> 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.
format	article
author	Débora Alves Joaquim Blesa Eric Duviella Lala Rajaoarisoa
author_facet	Débora Alves Joaquim Blesa Eric Duviella Lala Rajaoarisoa
author_sort	Débora Alves
title	Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information
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
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/327d213316c047bba49151e123849aaa
work_keys_str_mv	AT deboraalves robustdatadrivenleaklocalizationinwaterdistributionnetworksusingpressuremeasurementsandtopologicalinformation AT joaquimblesa robustdatadrivenleaklocalizationinwaterdistributionnetworksusingpressuremeasurementsandtopologicalinformation AT ericduviella robustdatadrivenleaklocalizationinwaterdistributionnetworksusingpressuremeasurementsandtopologicalinformation AT lalarajaoarisoa robustdatadrivenleaklocalizationinwaterdistributionnetworksusingpressuremeasurementsandtopologicalinformation
_version_	1718410454921904128

Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information

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