  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.
