Heuristic burst detection method using flow and pressure measurements

Abstract

Pipe bursts in a drinking water distribution system lead to water losses, interruption of supply, and damage to streets and houses due to the uncontrolled water flow. To minimize the negative consequences of pipe bursts, an early detection is necessary. This paper describes a heuristic burst detection method, which continuously compares measured and expected values of water demands and pressures. The expected values of the water demand are generated by an adaptive water demand forecasting model, and the expected values of the pressures are generated by a dynamic pressure drop-demand relation estimator. The method was tested off-line on a historic dataset of 5 years of water flow and pressure data in three supply areas (with 650, 11, 180 and 130, 920 connections) in the western part of the Netherlands. In the period 274 bursts were reported of which, based on the definition we propose in this paper, 38 were considered as relatively larger bursts. The method was able to detect 50, 25.9 and 7.8% in the considered areas related to all bursts, and around 80% in all three areas related to the subset of relatively larger bursts. The method generated false alarms on 3% of the evaluated days on average.