Statistical Methodology for PRV Malfunction Detection and Alerting in Water Distribution Networks: A large scale application to the city of Patras in western Greece.
Abstract
In the last twenty years, the necessity to manage losses in Water Distribution Networks (WDNs) has emerged as a consequence of freshwater shortages caused by the variability of climatic conditions, and the continuously increasing needs for drinking water due to its various uses. In the context of deescalating the environmental footprint of WDNs, pressure management strategies are widely adopted aiming at reducing water losses in the supply and distribution parts of water networks. Installation of Pressure Reducing Valves (PRVs) at critical locations of WDNs is an essential part of pressure regulation strategies, in order to reduce the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. Perdios et al. (2022) developed a novel statistical framework for early detection of PRV malfunctions that may significantly influence network's operation and the corresponding lifetime of related infrastructure. The approach was successfully implemented to an existing pressure management area (PMA) of the city of Patras in western Greece, leading to detection of critical malfunctions at least 2 days prior to flow disruptions. Herein, we calibrate and implement Perdios et al. (2022) approach, using pressure data for a 4 year period from 01/Jan./2017 to 26/Nov./2020, to a significant number of important PMAs of the WDN of the city of Patras, aiming at classifying the detected PRV malfunctions by decomposing the observed pressure deviations from the set point to systematic and random error components.
Acknowledgements The research work has been conducted within the project PerManeNt, which has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation under the call RESEARCH - CREATE - INNOVATE (project code: T2EDK-04177). References Perdios A., G. Kokosalakis, N. Th. Fourniotis, I. Karathanasi and A. Langousis (2022) Statistical framework for the detection of pressure regulation malfunctions and issuance of alerts in water distribution networks, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-022-02256-5- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H45I1485P