Publication
Nodal hydraulic head estimation through Unscented Kalman Filter for data-driven leak localization in water networks
Conference Article
Conference
IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes (SAFEPROCESS)
Edition
2024
Pages
67-72
Doc link
https://doi.org/10.1016/j.ifacol.2024.07.195
File
Abstract
In this paper, we present a nodal hydraulic head estimation methodology for water distribution networks (WDN) based on an Unscented Kalman Filter (UKF) scheme with application to leak localization. The UKF refines an initial estimation of the hydraulic state by considering the prediction model, as well as available pressure and demand measurements. To this end, it provides customized prediction and data assimilation steps. Additionally, the method is enhanced by dynamically updating the prediction function weight matrices. Performance testing on the Modena benchmark under realistic conditions demonstrates the method’s effectiveness in enhancing state estimation and data-driven leak localization.
Categories
control theory, nonlinear programming, optimisation.
Author keywords
leak localization, water distribution network, Unscented Kalman Filter, state interpolation
Scientific reference
L. Romero, P. Irofti, F. Stoican and V. Puig. Nodal hydraulic head estimation through Unscented Kalman Filter for data-driven leak localization in water networks, 2024 IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, 2024, Ferrara, pp. 67-72.
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