State and Parameter Estimation for Natural Gas Pipeline Networks Using Transient State Data

We formulate two estimation problems for pipeline systems in which measurements of the compressible gas flowing through a network of pipes are affected by time-varying injections, withdrawals, and compression. We consider a state estimation problem that is then extended to a joint state and paramete...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2019-09, Vol.27 (5), p.2110-2124
Main Authors: Sundar, Kaarthik, Zlotnik, Anatoly
Format: Article
Language:English
Subjects:
Compressibility
Computational modeling
Computer simulation
Economic models
Estimating techniques
Estimation
Formulations
Gas pipelines
Least squares method
Mathematical model
Natural gas
Noise measurement
Nonlinear equations
nonlinear least squares
optimization
Ordinary differential equations
Parameter estimation
Partial differential equations
Pipelines
State estimation
Time dependence
time periodicity
Transient analysis
transient flow
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Summary:We formulate two estimation problems for pipeline systems in which measurements of the compressible gas flowing through a network of pipes are affected by time-varying injections, withdrawals, and compression. We consider a state estimation problem that is then extended to a joint state and parameter estimation problem that can be used for data assimilation. In both formulations, the flow dynamics are described on each pipe by space- and time-dependent densities and mass flux which evolve according to a system of coupled partial differential equations, in which momentum dissipation is modeled using the Darcy-Wiesbach friction approximation. These dynamics are first spatially discretized to obtain a system of nonlinear ordinary differential equations on which state and parameter estimation formulations are given as nonlinear least squares problems. A rapid, scalable computational method for performing a nonlinear least squares estimation is developed. Extensive simulations and computational experiments on multiple pipeline test networks demonstrate the effectiveness of the formulations in obtaining state and parameter estimates in the presence of measurement and process noise.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2018.2851507