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

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