Study of Burst Alarming and Data Sampling Frequency in Water Distribution Networks

Abstract

In recent research, the residual of the Kalman filter applied to flow measurements in water distribution systems has been found to strongly correlate with bursts. A positive residual represents an unexpected extra flow, which is likely caused by an event such as a burst in the downstream network. However, there is a certain level of uncertainty in the flow measurements, leading to a noisy and fluctuating residual during normal and abnormal network operations. This work investigates how changes to a residual threshold affect the success rates of burst alarming during burst and nonburst periods, respectively, and presents a statistical method to automatically select a suitable residual threshold to tolerate uncertainty in flow measurements. In addition, this work also investigates how the number of burst alarms is influenced by using different flow measurement sampling frequencies and different averaging window sizes in the data sets. Engineered tests with three simulated burst events were conducted to validate the methods. The results showed that the threshold proposed in this study can produce a relatively high success rate in burst alarming. In addition, the results also showed that the current sampling interval of 15 min is suitable for burst detection from flow data, and that the use of a window averaged over the past several hours can reduce continual on/off alarm states.