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Robust Kalman Filter-Based Dynamic State Estimation of Natural Gas Pipeline Networks

Author

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  • Liang Chen
  • Peng Jin
  • Jing Yang
  • Yang Li
  • Yi Song

Abstract

To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper. Firstly, the dynamic state estimation model is built. Since the gas pipeline transient flow equations are less than the states, the boundary conditions are used as supplementary constraints to predict the transient states. To increase the measurement redundancy, the zero mass flow rate constraints at the sink nodes are taken as virtual measurements. Secondly, to ensure the stability under bad data condition, the robust Kalman filter algorithm is proposed by introducing a time-varying scalar matrix to regulate the measurement error variances correctly according to the innovation vector at every time step. At last, the proposed method is applied to a 30-node gas pipeline network in several kinds of measurement conditions. The simulation shows that the proposed robust dynamic state estimation can decrease the effects of bad data and achieve better estimating results.

Suggested Citation

  • Liang Chen & Peng Jin & Jing Yang & Yang Li & Yi Song, 2021. "Robust Kalman Filter-Based Dynamic State Estimation of Natural Gas Pipeline Networks," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, March.
  • Handle: RePEc:hin:jnlmpe:5590572
    DOI: 10.1155/2021/5590572
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    Cited by:

    1. Xueming Qian & Baotong Cui, 2022. "Mobile Sensor Networks for Finite-Time Distributed H ∞ Consensus Filtering of 3D Nonlinear Distributed Parameter Systems with Randomly Occurring Sensor Saturation," Mathematics, MDPI, vol. 10(17), pages 1-24, September.
    2. Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).

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