Abstract

Leaks in water distribution systems (WDSs) cause water and revenue losses, environmental pollution, and customer service disruptions. Thus, it is imperative to detect and locate leaks in WDSs. To identify leakages and locate them within a short distance from their actual locations, this paper proposes methods based on historical measurements and model simulation results. The methods consist of three main steps: model calibration, leak detection, and leak localization. In the model calibration step, water demand, pipe diameter and roughness were calibrated by performing extended period simulations with the use of genetic algorithms (GAs). In the leak detection step, finite difference methods were applied to the leakage time series in order to discern leakage signals in a WDS. Then, X-bar and cumulative sum control charts were employed, capturing the signals induced by pipe bursts and incipient leaks, respectively. With respect to leak localization, the search space of each detected leak was narrowed by selecting the most affected sensors (MASs) with the largest pressure residual. Then suspicious nodes were identified for each MAS by conducting sensitivity-to-leak analyses. Finally, each detected leak was located by a grid search coupled with an optimization-simulation method. The proposed methods were evaluated on a WDS of L-Town from the Battle of the Leakage Detection and Isolation Methods (BattLeDIM). Previous studies proposed time-consuming approaches for detecting and locating leaks, which would not be feasible for real complex systems. At most 13 out of 23 leaks were detected and located by previous work. The proposed approach was able to detect 12 out of 19 leaks only occurring in 2019, and 8 out of the 12 leaks were located within 300m of their actual locations. This approach can thus generate comparably accurate results improving computational efficiency.