Dynamical filtering equations for Stochastic Hybrid System state estimation

2012 ◽  
Author(s):  
Weiyi Liu ◽  
Inseok Hwang
Keyword(s):  
State Estimation ◽  
Hybrid System ◽  
System State ◽  
Stochastic Hybrid System

scholarly journals Efficient nonlinear filtering of a singularly perturbed stochastic hybrid system

2011 ◽  
Vol 14 ◽  
pp. 254-270 ◽  
Author(s):  
Jun H. Park ◽  
Boris Rozovskii ◽  
Richard B. Sowers
Keyword(s):  
State Estimation ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Nonlinear Filtering ◽  
Efficient Estimation ◽  
Singularly Perturbed ◽  
Stochastic Hybrid Systems ◽  
Stochastic Hybrid System ◽  
Estimation Scheme ◽  
On Line

AbstractOur focus in this work is to investigate an efficient state estimation scheme for a singularly perturbed stochastic hybrid system. As stochastic hybrid systems have been used recently in diverse areas, the importance of correct and efficient estimation of such systems cannot be overemphasized. The framework of nonlinear filtering provides a suitable ground for on-line estimation. With the help of intrinsic multiscale properties of a system, we obtain an efficient estimation scheme for a stochastic hybrid system.

POWER SYSTEM STATE ESTIMATION USING A ROBUST ESTIMATOR

2019 ◽  
Vol XVI (4) ◽  
pp. 53-65
Author(s):  
Zahid Khan ◽  
Katrina Lane Krebs ◽  
Sarfaraz Ahmad ◽  
Misbah Munawar
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Loss Function ◽  
Weighted Least Squares ◽  
Primary Data ◽  
Robust Estimator ◽  
System State ◽  
State Estimator ◽  
Suggested Approach ◽  
Power System State Estimation

State estimation (SE) is a primary data processing algorithm which is utilised by the control centres of advanced power systems. The most generally utilised state estimator is based on the weighted least squares (WLS) approach which is ineffective in addressing gross errors of input data of state estimator. This paper presents an innovative robust estimator for SE environments to overcome the non-robustness of the WLS estimator. The suggested approach not only includes the similar functioning of the customary loss function of WLS but also reflects loss function built on the modified WLS (MWLS) estimator. The performance of the proposed estimator was assessed based on its ability to decrease the impacts of gross errors on the estimation results. The properties of the suggested state estimator were investigated and robustness of the estimator was studied considering the influence function. The effectiveness of the proposed estimator was demonstrated with the help of examples which also indicated non-robustness of MWLS estimator in SE algorithm.

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