Filtering bad measurement data for water distribution system demand estimation

Doosun Kang, Kevin E Lansey

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Demand estimation has been solved by using a weighted least-squares (WLS) estimator incorporating field measurements with system simulation model. WLS estimator results are sensitive to spurious measurements caused by supervisory control and data acquisition malfunctions. Estimates using the contaminated measurements are not reliable and bad data should be filtered prior to demand estimation. This study presents a series of statistical methods to detect bad data, identify their locations, and correct the data values. The proposed methods are based on a linear measurement model that linearly relates state variables (nodal demands) to the field measurements (pipe flow rates). Application to a simple hypothetical network using synthetically generated data shows that the method can be successfully used as a preprocessing for single and multiple noninteracting bad data for reliable demand estimation.

Original languageEnglish (US)
Article number016003QWR
Pages (from-to)512-517
Number of pages6
JournalJournal of Water Resources Planning and Management
Volume136
Issue number4
DOIs
StatePublished - Jul 2010

Fingerprint

Water distribution systems
distribution system
water
demand
pipe flow
data acquisition
Pipe flow
system model
statistical method
simulation model
Data acquisition
Statistical methods
Flow rate
water distribution system
simulation
method
Values

Keywords

  • Bad data filtering
  • Demand estimation
  • Hypothesis test
  • SCADA systems
  • 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

Filtering bad measurement data for water distribution system demand estimation. / Kang, Doosun; Lansey, Kevin E.

In: Journal of Water Resources Planning and Management, Vol. 136, No. 4, 016003QWR, 07.2010, p. 512-517.

Research output: Contribution to journalArticle

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