scholarly journals Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography

2012 ◽  
Vol 48 (4) ◽  
Author(s):  
A. Schöniger ◽  
W. Nowak ◽  
H.-J. Hendricks Franssen
Keyword(s):  
Parameter Estimation ◽  
Kalman Filters ◽  
Ensemble Kalman Filters ◽  
Hydraulic Tomography

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

scholarly journals A Data-Driven Method for Improving the Correlation Estimation in Serial Ensemble Kalman Filters

2017 ◽  
Vol 145 (3) ◽  
pp. 985-1001 ◽  
Author(s):  
Michèle De La Chevrotière ◽  
John Harlim
Keyword(s):  
Kalman Filters ◽  
Data Driven ◽  
Training Procedure ◽  
Ensemble Kalman Filters ◽  
Regression Problem ◽  
Correlation Estimation ◽  
Sample Correlation ◽  
Correlation Statistics ◽  
Nonlinear Observation ◽  
Lorenz 96

A data-driven method for improving the correlation estimation in serial ensemble Kalman filters is introduced. The method finds a linear map that transforms, at each assimilation cycle, the poorly estimated sample correlation into an improved correlation. This map is obtained from an offline training procedure without any tuning as the solution of a linear regression problem that uses appropriate sample correlation statistics obtained from historical data assimilation outputs. In an idealized OSSE with the Lorenz-96 model and for a range of linear and nonlinear observation models, the proposed scheme improves the filter estimates, especially when the ensemble size is small relative to the dimension of the state space.

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.

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