Abstract

This paper addresses the problem of locating the optimal pressure measurement points in a hydraulic system to help system management, calibration/validation of hydraulic models and measurement planning. Two approaches are discussed in the present work. The first method splits the hydraulic system by means of community concept borrowed from graph theory and uses merely the topology of the network. The resulting subsystems will have minimum number of external and maximum number of internal connections and leaves the choice of locating the single pressure measurement location per subsystem to a higher-level decision. The second technique is based on the sensitivity analysis of the hydraulic network and places the measurement points at the most sensitive locations, while trying to preserve the spatial diversity of the layout, i.e. preventing the accumulation of the measurement points within a small area of high sensitivity. The performance of both techniques is demonstrated on real-size hydraulic networks. The proposed sampling layouts are compared to classic D-optimality, A-optimality and V-optimality criterion.

Highlights

  • Pressure data collection in water distribution systems (WDS) is essential for proper management of the system

  • 5 Summary In this paper the problem of optimal measurement layout design was investigated on real-life water distribution systems

  • The described algorithm is a slightly modified version of the Newman algorithm augmented with fine tuning embedded in a genetic algorithm to maximize modularity

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Summary

Introduction

Pressure data collection in water distribution systems (WDS) is essential for proper management of the system. One important branch of the researches focuses on capturing the presence of contamination (e.g. due to terrorist attack) as early as possible, see [2] Another approach aims to use the measurement for the calibration of a hydraulic simulation model, notably pipe roughness values and/or demand patterns. For such purposes, Walski [3] suggested to monitor the pressure at nodes with high base demand and on the perimeter of the skeletonised network. Yu and Powell [5] describe the problem as a multi-objective optimization, and used dynamic covariance matrix analysis to locate good sampling points. Behzadian et al [10] used multi-objective genetic algorithm and adaptive neural network to design a good sampling layout

Pressure Measurement Layout Design in WDSs
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