Kalman Filtering of Hydraulic Measurements for Burst Detection in Water Distribution Systems

TLDR

Automatic burst and leak detection in water distribution systems is crucial for water conservation and system management. This study proposes a novel burst detection method that applies adaptive Kalman filtering to hydraulic flow and pressure measurements at the district meter area level. The method models normal water usage or pressure with an adaptive Kalman filter, and uses the filter residual—the difference between predicted and measured flow—to flag abnormal usage indicative of bursts or leaks. Engineered flushing tests and real DMA data show that the filter residual strongly correlates with burst size, accurately identifies known incidents, and that flow measurements are more sensitive than pressure measurements.

Abstract

Automatic burst and leak detection in water distribution systems plays an important role in water saving and management. This research develops a novel burst detection method of using adaptive Kalman filtering on hydraulic measurements of flow and pressure at district meter area (DMA) level. Adaptive Kalman filtering is used to model normal water usage (or alternatively water pressure), so the residual of the filter (e.g., the difference between the predicted flow and the measured flow) represents the amount of abnormal water usage relating to the bursts (or newly occurred leaks) in the downstream network. The results from a series of engineered tests which simulated flushing show that the size of the bursts and leaks strongly correlates with the residual of the filter. Finally, the method was applied to data from several real DMAs in the north of England, and the results show that the detected bursts correspond well to known historical operational information such as customer complaints' records and work management (repair) data. The results suggest that flow measurement data are more sensitive to a burst or leak than the pressure measurement data.