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

被引:9
|
作者
Chen, Liang [1 ]
Jin, Peng [2 ]
Yang, Jing [2 ]
Li, Yang [3 ]
Song, Yi [4 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China
[2] State Grid Customer Serv Ctr, Tianjin 300309, Peoples R China
[3] Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China
[4] StateGrid Econ & Technol Res Inst Co Ltd, Beijing 102209, Peoples R China
关键词
OPTIMAL ENERGY-FLOW; TRANSIENT FLOW; SYSTEM; MODEL;
D O I
10.1155/2021/5590572
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页数:10
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