Statistical framework for the detection of pressure regulation malfunctions and issuance of alerts in water distribution networks

摘要

Abstract Pressure reducing valves (PRVs) are widely used to regulate pressures in the supply and distribution parts of water networks, by reducing the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. As all types of mechanical equipment, PRVs may exhibit malfunctions affecting pressure regulation, such as high frequency fluctuations around the set point and/or prolonged systematic deviations from the set point, allowing their detection to be approached in a statistical context. In this study, we develop a statistical framework for detection of PRV malfunctions in water supply and water distribution networks, which uses: (a) the root mean squared error as a proper statistical metric for monitoring the performance of PRVs by detecting individual malfunctions in high-resolution pressure time series, and (b) the hazard function concept to identify a proper duration of sequential events from (a) to issue alerts. The suggested methodology is implemented using pressure data at 1-min temporal resolution from pressure management area Diagora of the water distribution network of the city of Patras in Greece, for the 3-year period from 01/Jan./2017 to 31/Dec./2019. The obtained results show that the developed statistical approach effectively detects major PRV malfunctions as the issuance of alerts agrees well with the reported repair dates by the Municipal Enterprise of Water Supply and Sewerage of the City of Patras, allowing it to be used for operational purposes, while making it suitable for possible extensions to continuous monitoring and fault diagnosis of other types of mechanical equipment.