Water Distribution System Burst Detection Using a Nonlinear Kalman Filter

Jung, Donghwi; Lansey, Kevin

doi:10.1061/(asce)wr.1943-5452.0000464

Cited by 89 publications

(43 citation statements)

References 24 publications

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“…The sensor network layout (i.e., number and location of sensors) is assumed to be provided ("sensor network" in Figure 2) because the demand estimation is not a primary concern for decisions on the number and type of sensors and their locations. The KF-based WDS demand estimation methodology proposed by Jung and Lansey [8] is employed in this study because of its high accuracy. Based on the potential node groupings provided from the optimization algorithm (GA) submodule and field measurements obtained from the sensor network, the KF-based method estimates the node group demands.…”

Section: Methodsmentioning

confidence: 99%

“…Note that the KF-based estimation method has been used for real-time WDS demand estimation [2,8], whereas optimization techniques have been widely used for demand and its hourly pattern calibration, which estimates base demands and leakage (non-real-time estimation) [21][22][23][24]. Less estimation time is generally allowed for the former (e.g., 5 min), which results in using different methodologies for the two different estimation purposes.…”

Section: Introductionmentioning

confidence: 99%

“…They aggregated nodes in a study network into multiple groups to reduce the number of unknowns and to make the demand estimation problem overdetermined or even-determined, which was also suggested in [20]. Jung and Lansey [8] employed the nodal demand aggregation approach of Kang and Lansey in their KF-based WDS pipe burst detection model. They then demonstrated that using the same number of node groups as the number of flowmeters yielded the best accuracy with the KF-based demand estimation scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Previous demand forecasting/estimation studies have focused on estimating hourly to monthly macro-scale demand (i.e., total system demand) by using time series models [3][4][5][6]. A few studies have recently proposed micro-scale demand (i.e., nodal demands) estimation methodologies with a more precise time step (e.g., 15 min) based on coupling the Kalman filter (KF) with a hydraulic simulator (e.g., EPANET [7]) [2,8]. Advances in sensor technology, such as increased battery lifetime and communication frequency, have encouraged the development of demand estimation techniques compatible with field measurements from advanced sensors (e.g., advanced metering infrastructure (AMI)).…”

Section: Introductionmentioning

confidence: 99%

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Optimal Node Grouping for Water Distribution System Demand Estimation

Jung

Choi

Kim

2016

Water

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Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem. This study proposes an optimal node grouping model to maximize the real-time WDS demand estimation accuracy. This Kalman filter-based demand estimation method is linked with a genetic algorithm for node group optimization. The modified Austin network demand is estimated to demonstrate the proposed model. True demands and field measurements are synthetically generated using a hydraulic model of the study network. Accordingly, the optimal node groups identified by the proposed model reduce the total root-mean-square error of the estimated node group demand by 24% compared to that determined by engineering knowledge. Based on the results, more pipe flow sensors should be installed to measure small flows and to further enhance the demand estimation accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Node Grouping for Water Distribution System Demand Estimation

Jung

Choi

Kim

2016

Water

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“…According to the literature, normal distribution is the most popular probability distribution that used in hydraulic studies [13] [14] [25] [26]. We also use normal distribution for random number generation of D, C, and L.…”

Section: Example Applicationmentioning

confidence: 99%

Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems

Peng¹,

Mayorga²

2016

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This paper develops an innovative approach to optimize a long-term rehabilitation and upgrading schedule (RUS) for a water distribution system with considering both hydraulic failure and mechanical performance failure circumstances. The proposed approach assesses hydraulic reliability dynamically and then optimizes the long-term RUS in sequence for a water distribution system. The uncertain hydraulic parameters are treated as random numbers in a stochastic hydraulic reliability assessment. The methodologies used for optimization in a stochastic environment are: Monte Carlo Simulation, EPANET Simulation, Genetic Algorithms, Shamir and Howard's Exponential Model, Threshold Break Rate Model and Two-Stage Optimization Model. The proposed approach is conducted on a simulation model of water distribution network in a computer by two universal codes, namely the hydraulic reliability code and the optimal RUS code. The applicability of this approach is verified in an example of a benchmark water distribution network.

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