A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks
This work addresses leak detection and state estimation for water distribution management, which is crucial for urban sustainability, but it is incremental as it compares existing UKF variants.
The paper compared two data-driven state estimation methods based on the Unscented Kalman Filter for hydraulic state estimation and leak localization in water distribution networks, analyzing their accuracy and complexity and showing results on the L-TOWN benchmark.
The sustainability of modern cities highly depends on efficient water distribution management, including effective pressure control and leak detection and localization. Accurate information about the network hydraulic state is therefore essential. This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalman Filter (UKF), fusing pressure, demand and flow data for head and flow estimation. One approach uses a joint state vector with a single estimator, while the other uses a dual-estimator scheme. We analyse their main characteristics, discussing differences, advantages and limitations, and compare them theoretically in terms of accuracy and complexity. Finally, we show several estimation results for the L-TOWN benchmark, allowing to discuss their properties in a real implementation.