scholarly journals Algorithm of search identification with forced search for extremum

2021 ◽  
pp. 134
Author(s):  
A.I. Mikhalev ◽  
L.V. Korol
Keyword(s):  
System State ◽  
Identification Problems ◽  
State Criterion

We provide one of possible ways to reduce the time of transitive process of models tuning in the identification problems, which is: forced search for extremum of system state criterion.

Innovative Methods for Graphical Representation of System State Information

1995 ◽  
Author(s):  
Richard E. Maisano ◽  
Mark Y. Czarnolewski
Keyword(s):  
Graphical Representation ◽  
System State ◽  
State Information ◽  
Innovative Methods

TEMPERATURE MODELS FOR GRINDING SYSTEM STATE MONITORING

2019 ◽  
Vol 2 (3) ◽  
pp. 216-229
Author(s):  
Vasily Larshin ◽  
Natalia Lishchenko
Keyword(s):  
System State ◽  
State Monitoring ◽  
Grinding System

Identification Problems of Linear Stationary Systems. Part I. Prony's Method

2011 ◽  
Vol 43 (8) ◽  
pp. 1-18 ◽  
Author(s):  
Vladimir B. Larin ◽  
Alexander S. Apostolyuk
Keyword(s):  
Identification Problems ◽  
Prony's Method ◽  
Prony’S Method

A Family of Norms For System Identification Problems

1993 ◽  
Author(s):  
Mohammad-Ali Massoumnia ◽  
Robert L. Kosut
Keyword(s):  
System Identification ◽  
Identification Problems

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.

Conditionally minimax algorithm for nonlinear system state estimation

1994 ◽  
Vol 39 (8) ◽  
pp. 1617-1620 ◽  
Author(s):  
A.R. Pankov ◽  
A.V. Bosov
Keyword(s):  
Nonlinear System ◽  
State Estimation ◽  
System State

scholarly journals Bayesian recognition of a moving object information-measuring system state: a priori information weight correction

2021 ◽  
Vol 1027 ◽  
pp. 012021
Author(s):  
S M Muzhichek ◽  
G G Sebryakov ◽  
V I Pavlov ◽  
S V Tolstych ◽  
M B Kenzhekhanova
Keyword(s):  
A Priori ◽  
Moving Object ◽  
Measuring System ◽  
System State ◽  
A Priori Information ◽  
Information Measuring ◽  
Information Measuring System ◽  
Priori Information ◽  
Weight Correction
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