Evaluating Nonlinear Kalman Filters for Parameter Estimation in Reservoirs During Petroleum Well Drilling

2006 ◽  
Author(s):  
Gerhard Nygaard ◽  
Geir Naevdal ◽  
Saba Mylvaganam
Keyword(s):  
Parameter Estimation ◽  
Kalman Filters ◽  
Well Drilling

Parameter estimation of fully regulated synchronous generators using Unscented Kalman Filters

2019 ◽  
Vol 168 ◽  
pp. 210-217 ◽  
Author(s):  
M.A. González-Cagigal ◽  
J.A. Rosendo-Macías ◽  
A. Gómez-Expósito
Keyword(s):  
Parameter Estimation ◽  
Kalman Filters ◽  
Synchronous Generators

On-Line Performance Monitoring and Engine Diagnostic Using Robust Kalman Filtering Techniques

2003 ◽  
Author(s):  
Pierre Dewallef ◽  
Olivier Le´onard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Stochastic Methods ◽  
Inlet Temperature ◽  
State Variables ◽  
Present Contribution ◽  
Regression Problem ◽  
On Line

In this contribution, an on-line engine performance monitoring is carried out through an engine health parameter estimation based on several gas path measurements. This health parameter estimation makes use of the analytical redundancy of an engine model and therefore implies the knowledge of the engine state. As the latter is a priori not known the second task is therefore an engine state variable estimation. State variables here designate working conditions such as inlet temperature, pressure, Mach number, rotational speeds, … Estimation of the state variables constitutes a general application of the Extended Kalman Filter theory, while the health parameter estimation is a classical recurrent regression problem. Recent advances in stochastic methods [1] show that both problems can be solved by two Kalman filters working jointly. Such filters are usually named Dual Kalman Filters. The present contribution aims at using a dual Kalman filter modified to provide robustness. This procedure should be able to cope with as much as 20 to 30% of faulty data. The resulting online method is applied to a turbofan model developed in the frame of the OBIDICOTE 1 project. Several tests are carried out to check the performance monitoring capability and the robustness that can be achieved.

Parameter Estimation of Wind Turbines With PMSM Using Cubature Kalman Filters

2020 ◽  
Vol 35 (3) ◽  
pp. 1796-1804
Author(s):  
M. A. Gonzalez-Cagigal ◽  
Jose A. Rosendo-Macias ◽  
A. Gomez-Exposito
Keyword(s):  
Parameter Estimation ◽  
Wind Turbines ◽  
Kalman Filters

scholarly journals Unscented Kalman Filter for Unobservable Parameter Estimation in Heart Cell Signals

1970 ◽  
Vol 4 ◽  
pp. 27-28
Author(s):  
David Adolfo Sampedro-Puente ◽  
Jesús Fernández-Bes ◽  
Esther Pueyo
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Cell Membrane ◽  
Kalman Filters ◽  
Unscented Kalman Filter ◽  
Heart Cell ◽  
Channel Dynamics ◽  
Proarrhythmic Risk ◽  
Organ Level ◽  
Unobservable Parameter

One interesting feature of biological systems is that minor subcellular changes can cause alterations at the whole organ level. In the heart, the random dynamics of cell membrane ion channels contributes to beat-to-beat repolarization variability, which has been related to proarrhythmic risk. Inference of unobservable cellular parameters, such as the number of channels, is key to characterize such random ion channel dynamics. In this work, a methodology based on the use of Unscented Kalman Filters is proposed to infer the number of channel from action potential signals, like those commonly recorded experimentally.

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