State estimation network design for water distribution systems

Donghwi Jung, Joong Hoon Kim

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

State estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often aggregated to reduce the number of unknowns. To achieve high SE accuracy, the optimal observation locations in the WDS should be determined. This paper proposes an optimal meter placement and node grouping (OMPNG) model for WDS demand estimation (DE). The nonlinear Kalman filter (NKF) method is used to estimate the nodal group demand (NGD) from pipe flow measurements at meter locations. A k-means clustering method is introduced to generate the initial node grouping for the proposed OMPNG model. An elitism-based genetic algorithm is employed to minimize the sum of the NGD root-mean-square errors (RMSEs). The proposed OMPNG model was applied to the modified Austin network DE problem, and the results were compared with those obtained by optimizing node grouping with fixed meter locations based only on engineering sense. The results showed that the proposed OMPNG model significantly improves the DE accuracy and reliability.

Original languageEnglish
Article number06017006
JournalJournal of Water Resources Planning and Management
Volume144
Issue number1
DOIs
Publication statusPublished - 2018 Jan 1

Fingerprint

Water distribution systems
network design
distribution system
State estimation
grouping
water
demand
pipe flow
flow measurement
Kalman filter
genetic algorithm
Pipe flow
Flow measurement
Kalman filters
Mean square error
water distribution system
Group
engineering
Genetic algorithms

ASJC Scopus subject areas

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

Cite this

State estimation network design for water distribution systems. / Jung, Donghwi; Kim, Joong Hoon.

In: Journal of Water Resources Planning and Management, Vol. 144, No. 1, 06017006, 01.01.2018.

Research output: Contribution to journalArticle

@article{aff2354c10af48ae90d27bb7a58ac05e,
title = "State estimation network design for water distribution systems",
abstract = "State estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often aggregated to reduce the number of unknowns. To achieve high SE accuracy, the optimal observation locations in the WDS should be determined. This paper proposes an optimal meter placement and node grouping (OMPNG) model for WDS demand estimation (DE). The nonlinear Kalman filter (NKF) method is used to estimate the nodal group demand (NGD) from pipe flow measurements at meter locations. A k-means clustering method is introduced to generate the initial node grouping for the proposed OMPNG model. An elitism-based genetic algorithm is employed to minimize the sum of the NGD root-mean-square errors (RMSEs). The proposed OMPNG model was applied to the modified Austin network DE problem, and the results were compared with those obtained by optimizing node grouping with fixed meter locations based only on engineering sense. The results showed that the proposed OMPNG model significantly improves the DE accuracy and reliability.",
author = "Donghwi Jung and Kim, {Joong Hoon}",
year = "2018",
month = "1",
day = "1",
doi = "10.1061/(ASCE)WR.1943-5452.0000862",
language = "English",
volume = "144",
journal = "Journal of Water Resources Planning and Management - ASCE",
issn = "0733-9496",
publisher = "American Society of Civil Engineers (ASCE)",
number = "1",

}

TY - JOUR

T1 - State estimation network design for water distribution systems

AU - Jung, Donghwi

AU - Kim, Joong Hoon

PY - 2018/1/1

Y1 - 2018/1/1

N2 - State estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often aggregated to reduce the number of unknowns. To achieve high SE accuracy, the optimal observation locations in the WDS should be determined. This paper proposes an optimal meter placement and node grouping (OMPNG) model for WDS demand estimation (DE). The nonlinear Kalman filter (NKF) method is used to estimate the nodal group demand (NGD) from pipe flow measurements at meter locations. A k-means clustering method is introduced to generate the initial node grouping for the proposed OMPNG model. An elitism-based genetic algorithm is employed to minimize the sum of the NGD root-mean-square errors (RMSEs). The proposed OMPNG model was applied to the modified Austin network DE problem, and the results were compared with those obtained by optimizing node grouping with fixed meter locations based only on engineering sense. The results showed that the proposed OMPNG model significantly improves the DE accuracy and reliability.

AB - State estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often aggregated to reduce the number of unknowns. To achieve high SE accuracy, the optimal observation locations in the WDS should be determined. This paper proposes an optimal meter placement and node grouping (OMPNG) model for WDS demand estimation (DE). The nonlinear Kalman filter (NKF) method is used to estimate the nodal group demand (NGD) from pipe flow measurements at meter locations. A k-means clustering method is introduced to generate the initial node grouping for the proposed OMPNG model. An elitism-based genetic algorithm is employed to minimize the sum of the NGD root-mean-square errors (RMSEs). The proposed OMPNG model was applied to the modified Austin network DE problem, and the results were compared with those obtained by optimizing node grouping with fixed meter locations based only on engineering sense. The results showed that the proposed OMPNG model significantly improves the DE accuracy and reliability.

UR - http://www.scopus.com/inward/record.url?scp=85034053062&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034053062&partnerID=8YFLogxK

U2 - 10.1061/(ASCE)WR.1943-5452.0000862

DO - 10.1061/(ASCE)WR.1943-5452.0000862

M3 - Article

AN - SCOPUS:85034053062

VL - 144

JO - Journal of Water Resources Planning and Management - ASCE

JF - Journal of Water Resources Planning and Management - ASCE

SN - 0733-9496

IS - 1

M1 - 06017006

ER -