Robust Kalman filter-based dynamic state estimation of natural gas pipeline networks

TL;DR

This paper introduces a robust Kalman filter-based method for accurate dynamic state estimation in large-scale natural gas pipeline networks, effectively handling bad data and non-zero mean noises.

Contribution

It develops a novel robust Kalman filter approach using boundary conditions and virtual measurements to improve state estimation under noisy conditions.

Findings

Reduces the impact of bad data on state estimation.

Achieves more accurate transient state predictions.

Demonstrates effectiveness on a 30-node pipeline network.

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 networks 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.