Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization

作     者：Romero-Ben, Luis Irofti, Paul Stoican, Florin Puig, Vicenç 

作者机构：Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain LOS-CS-FMI University of Bucharest Romania  The Universitat Politècnica de Catalunya Spain Dept. of Automation Control and Systems Engineering Politehnica University of Bucharest Romania 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Kalman filters 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.