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

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.