Detection and pre-localization of anomalous consumption events in water distribution networks through automated, pressure-based methodology

Anomalous water-consumption events (AEs) can significantly impact the functioning of water distribution networks, and their prompt identification can improve the service provided by water utilities. This study proposes a new methodology for AE detection and pre-localization in water distribution net...

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Veröffentlicht in:Water resources and industry 2024-06, Vol.31, p.100255, Article 100255
Hauptverfasser: Mazzoni, Filippo, Marsili, Valentina, Alvisi, Stefano, Franchini, Marco
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Sprache:eng
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Zusammenfassung:Anomalous water-consumption events (AEs) can significantly impact the functioning of water distribution networks, and their prompt identification can improve the service provided by water utilities. This study proposes a new methodology for AE detection and pre-localization in water distribution networks relying exclusively on pressure-data collected in the field, which are exploited to evaluate differential-pressure trends for all possible pressure-sensors couples located in the WDN. In greater detail, AEs are detected and pre-localized by analysing differential-pressure trends over time. The level of deviation of these trends from the standard is considered to provide information about (i) AE alert levels and (ii) the area of the network where the AE is most likely to occur. The application of the methodology to two real case studies featuring different characteristics in terms of residential and industrial users demonstrated method effectiveness in detecting and pre-localizing individual and simultaneous AEs of different magnitude and occurring at different times of the day, providing useful information about the presence of AEs without the need for hydraulic models, and allowing the evaluation of their effects in terms of piezometric head alteration in the different areas of the system. [Display omitted] •A method to identify anomalous events in water networks was developed.•The method relies exclusively on pressure data collected in the field.•Anomalous events are detected based on differential-pressure alterations.•Information about the area(s) in which events are likely to occur is provided.•The method can identify simultaneous events occurring in different network areas.
ISSN:2212-3717
2212-3717
DOI:10.1016/j.wri.2024.100255