A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks

TL;DR

This paper compares joint and dual UKF methods for water network state estimation and leak localization, analyzing their accuracy, complexity, and practical performance using the L-TOWN benchmark.

Contribution

It provides a detailed comparison of joint and dual UKF implementations for water network state estimation, highlighting their advantages and limitations.

Findings

Dual UKF offers better accuracy in leak localization.

Joint UKF has lower computational complexity.

Both methods perform effectively in the L-TOWN benchmark.

Abstract

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.