Work

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

Year: 2025

Type: article

Abstract: Leakage in water systems causes significant daily losses, degraded service, increased costs, and environmental problems. Efficient leak localization is crucial to minimizing these impacts. Most leak management methods rely solely on pressure sensors due to their reduced cost and ease of installation, potentially missing other sources of valuable data. This article proposes a data-driven hydraulic state estimation methodology based on a dual unscented Kalman filter (UKF) approach, which enables the fusion of pressure, flow, and demand measurements. In this way, the presented method enhances the estimation of both nodal hydraulic heads, critical in localization tasks, and pipe flows, useful for operational purposes. The strategy is evaluated in well-known open source case studies, namely Modena and L-TOWN. In terms of interpolation accuracy, the average performance in both scenarios shows a ~52% error reduction in pressure estimation if comparing with methods only using pressure sensors, and a ~24% error reduction in flow estimation if comparing with a nondual UKF implementation. Moreover, leak localization results in L-TOWN show an average distance error reduction of ~14%, with impressive results in a multileak scenario where a ~68% reduction is reached.

Source: IEEE Transactions on Control Systems Technology

DOI: 10.1109/tcst.2025.3610975

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

Institutions: Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya, University of Bucharest, Universitatea Națională de Știință și Tehnologie Politehnica București

Language: English

Topic: Water Systems and Optimization

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Clean water and sanitation, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645