Abstract
This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.
Highlights
Water distribution networks are complex systems that are difficult to manage and monitor with extreme importance nowadays
The evaluation of the performance of the proposed leak localization method at node level defined in Equation (21) will be analyzed utilizing Average Topological Distance (ATD) [11]
We show that the leak localization performance reached an ATD of 8 and 5.5 nodes with 5 and 10 inner pressure sensors installed in the network respectively
Summary
Water distribution networks are complex systems that are difficult to manage and monitor with extreme importance nowadays. Unlike hardware-based methods, these methods do not seek to locate the leak point accurately but minimize possible leakage areas Since these methods are based on information, such as the pressure of the pipe network, flow data, and so forth, they work well on any type of pipe. These methods can be divided into physical modeling methods and datadriven methods. It is possible to identify potential areas of the leak based on certain rules or principles without resorting to the simulation of the physical model results [17] These methods need, in general, an important number of non-leak and leak data scenarios in the training process to obtain reasonable results. The term HTh is the pressure drop across the pipes due to the difference in geodesic level (i.e., elevation) in meters [m] between the ends of the pipes with h ∈ Rn the vector of geodesic levels at each vertex
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