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Related Experiment Video

Updated: Apr 7, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Distributed and decentralized state estimation in gas networks as distributed parameter systems.

Hesam Ahmadian Behrooz1, R Bozorgmehry Boozarjomehry1

  • 1Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.

ISA Transactions
|July 4, 2015
PubMed
Summary

A new distributed and decentralized state estimation framework improves real-time performance for gas transmission networks (GTNs). This method enhances computational efficiency by 10x in real-world scenarios without significant accuracy loss.

Keywords:
Decentralized state estimationGas transmissionKalman filteringLarge-scale systems

Related Experiment Videos

Last Updated: Apr 7, 2026

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Area of Science:

  • Engineering
  • Control Systems
  • Computational Science

Background:

  • Centralized Kalman filters face significant disadvantages for large-scale systems like gas transmission networks (GTNs).
  • Dynamic behavior simulation in GTNs requires complex non-isothermal models including mass, momentum, and energy balance equations.
  • Decentralized estimation is crucial for managing the complexity of high-pressure, long-distance GTNs.

Purpose of the Study:

  • To propose a novel framework for distributed and decentralized state estimation in gas transmission networks.
  • To extend the continuous/discrete Extended Kalman Filter for distributed and decentralized estimation (DDE) to GTNs.
  • To enhance computational efficiency and real-time performance for state estimation in large-scale pipeline systems.

Main Methods:

  • Decomposition of the global GTN model into several interconnected local subsystems.
  • Modification of Kalman filter assimilation and prediction steps to account for overlapping and external states within local models.
  • Construction of local dynamic Riccati equations for each subsystem, accepting a maximum 5% error in state standard deviation.

Main Results:

  • The proposed DDE methodology demonstrates comparable accuracy to centralized Kalman filters on benchmark networks.
  • A maximum error of 5% in the estimated standard deviation of states was observed due to local model construction.
  • Real-time factor for state estimation in a real-life GTN was increased by a factor of 10.

Conclusions:

  • The distributed and decentralized state estimation framework offers a significant improvement in computational efficiency for GTNs.
  • The method achieves a substantial increase in the real-time factor (10x) with minimal impact on estimation accuracy.
  • This approach provides a viable solution for real-time state estimation challenges in large-scale gas transmission networks.