Multi-objective optimization of pressure sensor placement for leakage location and network calibration

Abstract

Optimal pressure sensor placement is important for several purposes, including leakage detection, model calibration and state estimation. However, pressure data are required to serve multiple objectives simultaneously, and a versatile methodology to optimize sensor locations across varying network scales remains lacking. Existing approaches, mainly validated in small-scale networks, often face computational inefficiencies when applied to larger systems, limiting their practical utility. To address these gaps, this paper proposes a Projection-Sensitivity Based Multi-Optimal Sensor Placement (PSMOSP) framework. It is designed to identify robust and adaptable monitoring points for water distribution networks (WDNs) of diverse sizes. The framework uses a multi-objective genetic algorithm to resolve the Pareto frontier. There are two key objectives: more accurate calibration of model parameters and leakage localization. It improves computational efficiency through an analytical approach that constructs the pressure-roughness sensitivity matrix and parallel computation. Case studies on networks of different scales validate the framework, showing that it outperforms traditional methods in leak location and calibration accuracy, as well as spatial coverage. The results highlight how sensitivity-driven calibration and projection-based localization enhance the robustness of sensor placement in large-scale WDNs, offering a generalized methodology for online monitoring for tasks like pressure estimation, model calibration, and anomaly detection.