Optimal meter placement for water distribution system state estimation

Doosun Kang, Kevin E Lansey

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Real-time state estimates (SEs) of nodal demands in a water distribution system (WDS) can be developed using data from a supervisory control and data acquisition (SCADA) system. These estimates provide information for improved operations and customer service in terms of energy consumption and water quality. The SE results in a WDS are significantly affected by measurement characteristics, i.e., meter types, numbers, and topological distributions. The number and type of meters are generally selected prior to a SCADA layout. Thus, selecting measurement locations is critical. The aim of this study is to develop a methodology that optimally locates field measurement sites and leads to more reliable SEs. An optimal meter placement (OMP) problem is posed as a multiobjective optimization form. Three distinctive objectives are formulated: (1) minimization of nodal demand estimation uncertainty; (2) minimization of nodal pressure prediction uncertainty; and (3) minimization of absolute error between demand estimates and their expected values. Objectives (1) and (2) represent the model precisions while Objective (3) describes the model accuracy. The OMP is solved using a multiobjective genetic algorithm (MOGA) based on Pareto-optimal solutions. The trade-off between model precision and accuracy is clearly observed in two case studies and it is recommended to use both criteria as objectives. It is also concluded that the proposed objectives are more appropriate for OMP purposes compared to calibration sampling design studies in which minimization of metering costs (i.e., number of meters) is used as one of the multiple objectives. The MOGA saves computational effort while providing optimal Pareto solutions compared to full enumeration for a small hypothetical network. For real networks, GA solutions, although not guaranteed to be globally optimal, are improvements over those obtained using less robust methods or designers' experienced judgment.

Original languageEnglish (US)
Article number008003QWR
Pages (from-to)337-347
Number of pages11
JournalJournal of Water Resources Planning and Management
Volume136
Issue number3
DOIs
StatePublished - May 2010

Fingerprint

Water distribution systems
distribution system
State estimation
genetic algorithm
water
data acquisition
trade-off
uncertainty
Genetic algorithms
demand
calibration
water quality
Plant layout
SCADA systems
energy consumption
layout
methodology
sampling
Multiobjective optimization
prediction

Keywords

  • Multiple objective genetic algorithms
  • Optimal meter placement
  • SCADA system
  • State estimation
  • Water distribution

ASJC Scopus subject areas

  • Water Science and Technology
  • Civil and Structural Engineering
  • Management, Monitoring, Policy and Law
  • Geography, Planning and Development

Cite this

Optimal meter placement for water distribution system state estimation. / Kang, Doosun; Lansey, Kevin E.

In: Journal of Water Resources Planning and Management, Vol. 136, No. 3, 008003QWR, 05.2010, p. 337-347.

Research output: Contribution to journalArticle

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