CCWI2017: F79 'Identifiability analysis for pressure sensors positioning'

Tarantino, S.

Risultato della ricerca: Paper

Abstract

The identifiability analysis is investigated as sampling design method aimed to the leakage detection in looped water distribution networks. The preliminary ranking of the candidate nodes for the pressure sensors positioning is performed by running several hydraulic simulations and calculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used to perform the identifiability analysis by calculating the collinearity index which provides the maximum number of sensors and their location into the network. The index selects the nodes according to their sensitivities to several leakages scenarios, simulated in EPANET by changing the emitter coefficient of the leakages function both with a One-At-Time and Monte Carlo approach. The collinearity index also identifies the subset of the pressure monitoring nodes with the lowest correlation (redundancy) between the measurements. The method is applied to the benchmark network Apulian.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2017

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CCWI2017: F79 'Identifiability analysis for pressure sensors positioning'. / Tarantino, S.

2017.

Risultato della ricerca: Paper

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title = "CCWI2017: F79 'Identifiability analysis for pressure sensors positioning'",
abstract = "The identifiability analysis is investigated as sampling design method aimed to the leakage detection in looped water distribution networks. The preliminary ranking of the candidate nodes for the pressure sensors positioning is performed by running several hydraulic simulations and calculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used to perform the identifiability analysis by calculating the collinearity index which provides the maximum number of sensors and their location into the network. The index selects the nodes according to their sensitivities to several leakages scenarios, simulated in EPANET by changing the emitter coefficient of the leakages function both with a One-At-Time and Monte Carlo approach. The collinearity index also identifies the subset of the pressure monitoring nodes with the lowest correlation (redundancy) between the measurements. The method is applied to the benchmark network Apulian.",
author = "{Tarantino, S.} and {La Loggia}, Goffredo and Gabriele Freni and Valeria Puleo",
year = "2017",
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T1 - CCWI2017: F79 'Identifiability analysis for pressure sensors positioning'

AU - Tarantino, S.

AU - La Loggia, Goffredo

AU - Freni, Gabriele

AU - Puleo, Valeria

PY - 2017

Y1 - 2017

N2 - The identifiability analysis is investigated as sampling design method aimed to the leakage detection in looped water distribution networks. The preliminary ranking of the candidate nodes for the pressure sensors positioning is performed by running several hydraulic simulations and calculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used to perform the identifiability analysis by calculating the collinearity index which provides the maximum number of sensors and their location into the network. The index selects the nodes according to their sensitivities to several leakages scenarios, simulated in EPANET by changing the emitter coefficient of the leakages function both with a One-At-Time and Monte Carlo approach. The collinearity index also identifies the subset of the pressure monitoring nodes with the lowest correlation (redundancy) between the measurements. The method is applied to the benchmark network Apulian.

AB - The identifiability analysis is investigated as sampling design method aimed to the leakage detection in looped water distribution networks. The preliminary ranking of the candidate nodes for the pressure sensors positioning is performed by running several hydraulic simulations and calculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used to perform the identifiability analysis by calculating the collinearity index which provides the maximum number of sensors and their location into the network. The index selects the nodes according to their sensitivities to several leakages scenarios, simulated in EPANET by changing the emitter coefficient of the leakages function both with a One-At-Time and Monte Carlo approach. The collinearity index also identifies the subset of the pressure monitoring nodes with the lowest correlation (redundancy) between the measurements. The method is applied to the benchmark network Apulian.

UR - http://hdl.handle.net/10447/355268

M3 - Paper

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