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2017 | OriginalPaper | Chapter

9. Features of Demand Patterns for Leak Detection in Water Distribution Networks

Authors : Marcos Quiñones-Grueiro, Cristina Verde, Orestes Llanes-Santiago

Published in: Modeling and Monitoring of Pipelines and Networks

Publisher: Springer International Publishing

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Abstract

This chapter presents a data-driven based approach for detection of leaks in water distribution networks in which the demand is formed by a known periodic pattern plus a stochastic variable. The leak detection method is based on an adaptation of the dynamic principal component analysis (DPCA), and it is assumed that only pressures at selected consumption nodes are measured. Since the variables of water distribution networks (WDNs), even in normal conditions, are nonstationary and time-correlated the data are preprocessed with a periodic transformation previous to the application of DPCA. The proposed approach is validated with the Hanoi network model. The performance is evaluated with three indexes: the leak detection rate, the false alarm rate, and the delay of the detection with respect to the leak’s occurrence time. All of them are satisfactory for diverse leaks’ scenarios, and the proposed approach presents an improvement in the leak detection rate of approximately \(70\%\) as compared with the traditional PCA and DPCA methods.

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Metadata
Title
Features of Demand Patterns for Leak Detection in Water Distribution Networks
Authors
Marcos Quiñones-Grueiro
Cristina Verde
Orestes Llanes-Santiago
Copyright Year
2017
Book
Modeling and Monitoring of Pipelines and Networks

Print ISBN: 978-3-319-55943-8

Electronic ISBN: 978-3-319-55944-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-55944-5_9

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