Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization
This addresses water loss problems for utility operators, but appears incremental as it builds on existing sensor fusion methods.
The paper tackles leak localization in water networks by proposing a dual Unscented Kalman Filter approach that fuses pressure, flow, and demand sensor data, showing improved interpolation accuracy and more precise leak localization in benchmark case studies like L-TOWN.
Leakage in water systems results in significant daily water losses, degrading service quality, increasing costs, and aggravating environmental problems. Most leak localization methods rely solely on pressure data, missing valuable information from other sensor types. This article proposes a hydraulic state estimation methodology based on a dual Unscented Kalman Filter (UKF) approach, which enhances the estimation of both nodal hydraulic heads, critical in localization tasks, and pipe flows, useful for operational purposes. The approach enables the fusion of different sensor types, such as pressure, flow and demand meters. The strategy is evaluated in well-known open source case studies, namely Modena and L-TOWN, showing improvements over other state-of-the-art estimation approaches in terms of interpolation accuracy, as well as more precise leak localization performance in L-TOWN.