An iterative dynamic state estimation and bad data processing

1989 ◽  
Vol 11 (1) ◽  
pp. 70-74 ◽  
Author(s):  
N Bretas
Keyword(s):  
Data Processing ◽  
State Estimation ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Bad Data

An Efficient Dynamic State Estimation Algorithm Including Bad Data Processing

1987 ◽  
Vol PER-7 (11) ◽  
pp. 49-49 ◽  
Author(s):  
A. M. Leite da Silva ◽  
M. B. Do Coutto Filho ◽  
J. M. C. Cantera
Keyword(s):  
Data Processing ◽  
State Estimation ◽  
Estimation Algorithm ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Bad Data

An Efficient Dynamic State Estimation Algorithm including Bad Data Processing

1987 ◽  
Vol 2 (4) ◽  
pp. 1050-1058 ◽  
Author(s):  
A. M. Leite da Silva ◽  
M. B. Do Coutto Filho ◽  
J. M. C. Cantera
Keyword(s):  
Data Processing ◽  
State Estimation ◽  
Estimation Algorithm ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Bad Data

Dynamic state estimation including bad data suppression for electric power systems

1979 ◽  
Vol 99 (4) ◽  
pp. 33-44 ◽  
Author(s):  
Ken-Ichi Nishiya ◽  
Hiroshi Takagi ◽  
Jun Hasegawa ◽  
Toichiro Koike
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Electric Power ◽  
Electric Power Systems ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Bad Data

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.

Bad Data Detection by the Methods of Dynamic State Estimation for on-line Control of Electric Power System Operation

2017 ◽  
pp. 18-27
Author(s):  
Anna Mikhailovna GLAZUNOVA ◽  
◽  
Irina Nikolayevna KOLOSOK ◽  
Yevguenii Sergueyevich S"YEMSHCHIKOV ◽  
◽  
...  
Keyword(s):  
State Estimation ◽  
Electric Power System ◽  
Dynamic State ◽  
Data Detection ◽  
System Operation ◽  
Power System Operation ◽  
Bad Data Detection ◽  
Dynamic State Estimation ◽  
On Line ◽  
Bad Data
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