Robust Kalman Filter with Application to State Estimation of a Nuclear Reactor

Kalman Filter ◽  
10.5772/9595 ◽  
2010 ◽  
Author(s):  
Zhe Dong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Nuclear Reactor ◽  
Robust Kalman Filter

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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liang Chen ◽  
Peng Jin ◽  
Jing Yang ◽  
Yang Li ◽  
Yi Song
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Transient Flow ◽  
Gas Pipeline ◽  
Dynamic State ◽  
Flow Equations ◽  
Pipeline Networks ◽  
Robust Kalman Filter ◽  
Dynamic State Estimation ◽  
Bad Data

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.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

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