What is it about?
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.
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Why is it important?
The results show that most of the filter coefficient values of the proposed method are smaller than the traditional Kalman filter in the normal measurement condition. This means that the filtering efficiency of the robust method is better. Furthermore, the two methods are studied under the bad data and non-zero mean measurement noises conditions, and the results show that the proposed method can decrease the effects of bad data, obtaining accurate estimating states under these two conditions.
Perspectives
To cope with the above problems, and obtain the accurate transient states of large scale gas pipeline networks under practical operating conditions, this paper focuses on the robust dynamic state estimation method along with the following contributions: 1) To deal with the problem that the transient equations are less than the states, the boundary conditions are used as supplementary constraints, and the linear process functions are established. The zero mass flow rates at the sink nodes are taken as virtual measurements, improving the measurement redundancy. 2) A time-varying scalar matrix is proposed to regulate the measurement variance matrix according to the innovations, making that the measurements can correct the predicted states accurately, and then increasing the robustness of the Kalman filter against the bad data and non-zero mean noises. 3) The proposed dynamic state estimation method is tested on a 30-node natural gas pipeline network under the normal measurement condition, bad data condition and non-zero mean noises condition. The simulation results show that the proposed approach manages to perform robust DSE of natural gas pipeline networks and the performance of the proposed method is better than the traditional Kalman filter.
Professor/PhD Supervisor/SMIEEE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: Robust Kalman Filter-Based Dynamic State Estimation of Natural Gas Pipeline Networks, Mathematical Problems in Engineering, March 2021, Hindawi Publishing Corporation,
DOI: 10.1155/2021/5590572.
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