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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Acknowledgements
Project supported by the Mexican Government Scholarship Program for International Students. In addition, the authors acknowledge the support provided by DGAPA-UNAM IT100716, II-UNAM and Universidad Tecnológica de la Habana José Antonio Echeverría (CUJAE).
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Quiñones-Grueiro, M., Verde, C., Llanes-Santiago, O. (2017). Features of Demand Patterns for Leak Detection in Water Distribution Networks. In: Verde, C., Torres, L. (eds) Modeling and Monitoring of Pipelines and Networks. Applied Condition Monitoring, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-55944-5_9
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DOI: https://doi.org/10.1007/978-3-319-55944-5_9
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