Application of Linear Observers to Estimate States of a Roll Simulator

2014 ◽  
Author(s):  
Scott B. Zagorski ◽  
Gary J. Heydinger ◽  
Dennis A. Guenther
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Kalman Filters ◽  
Disturbance Observer ◽  
Gain Scheduling ◽  
System Parameters ◽  
Scheduling Scheme

In this research, a variety of Kalman Filters are implemented in an effort to estimate sled speed of a Roll Simulator. An Extended Kalman Filter (EKF) is incorporated to capture the nonlinear dynamics of the sled-platform assembly to estimate sled speed for the entire motion, as a linear Kalman Filter was found to be inadequate. When applied to experimental data, the EKF over-estimates sled speed, which is due to a disturbance force and/or uncertainty in system parameters. In combination with the disturbance observer, the Kalman Filter adequately estimates sled speed for experimental data. For lower speed/payload applications, a Kalman Filter using an accelerometer and measured drum speed is able to accurately track sled speed when a gain scheduling scheme is employed.

Investigating Fault Detection and Diagnosis in a Hydraulic Pitch System Using a State Augmented EKF-Approach

2019 ◽  
Author(s):  
Magnus F. Asmussen ◽  
Henrik C. Pedersen ◽  
Lina Lilleengen ◽  
Andreas Larsen ◽  
Thomas Farsakoglou
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
System Parameters ◽  
Power Regulation ◽  
Pitch System ◽  
Detection And Diagnosis ◽  
Drop Outs

Abstract Pitch systems impose an important part of today’s wind turbines, where they are both used for power regulation and serve as part of a turbines safety system. Any failure on a pitch system is therefore equal to an increase in downtime of the turbine and should hence be avoided. By implementing a Fault Detection and Diagnosis (FDD) scheme faults may be detected and estimated before resulting in a failure, thus increasing the availability and aiding in the maintenance of the wind turbine. The focus of this paper is therefore on the development of a FDD algorithm to detect leakage and sensor faults in a fluid power pitch system. The FDD algorithm is based on a State Augmented Extended Kalman Filter (SAEKF) and a bank of observers, which is designed utilizing an experimentally validated model of a pitch system. The SAEKF is designed to detect and estimate both internal and external leakage faults, while also estimating the unknown external load on the system, and the bank of observers to detect sensor drop-outs. From simulation it is found that the SAEKF may detect both abrupt and evolving internal and external leakages, while being robust towards noise and variation in system parameters. Similar it is found that the scheme is able to detect sensor drop-outs, but is less robust towards this.

Extended Kalman Filter for Reconstruction of a Nonlinear Thermofluid System

1986 ◽  
Vol 108 (2) ◽  
pp. 156-158 ◽  
Author(s):  
R. Shoureshi ◽  
K. McLaughlin
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Heat Pump ◽  
Extended Kalman Filter ◽  
Eigenvalue Distribution ◽  
Integration Time ◽  
Time Step ◽  
Filter Technique ◽  
Gain Matrix ◽  
Good Agreement

Extended Kalman filter technique is used to develop an observer for a nonlinear thermofluid system, namely a heat pump. The observer’s optimal gain matrix is designed based on the eigenvalue distribution, integration time step, and stability of the system along a desired trajectory. The observer response is compared with experimental data and very good agreement is obtained.

Automotive Battery Prognostics Using Dual Extended Kalman Filter

2009 ◽  
Author(s):  
Matteo Rubagotti ◽  
Simona Onori ◽  
Giorgio Rizzoni
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Future Evolution ◽  
On Line ◽  
Automotive Batteries ◽  
Useful Life ◽  
Simulation Results

This paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approach.

Calibrating multi-machine power system parameters with the extended Kalman filter

2011 ◽  
Author(s):  
Karanjit Kalsi ◽  
Yannan Sun ◽  
Zhenyu Huang ◽  
Pengwei Du ◽  
Ruisheng Diao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Power System ◽  
Extended Kalman Filter ◽  
System Parameters

CALIBRATION OF MULTIFACTOR MODELS IN ELECTRICITY MARKETS

2004 ◽  
Vol 07 (02) ◽  
pp. 101-120 ◽  
Author(s):  
MARTIN BARLOW ◽  
YURI GUSEV ◽  
MANPO LAI
Keyword(s):  
Kalman Filter ◽  
Stochastic Processes ◽  
Extended Kalman Filter ◽  
Electricity Markets ◽  
Kalman Filters ◽  
Historical Data ◽  
Spot Price ◽  
Market Data ◽  
Multifactor Models ◽  
Spot Prices

Spot prices of electricity and other energy commodities are often modeled by multifactor stochastic processes. This poses a problem of estimating models' parameters based on historical data, i.e. calibrating them to markets. Here we show how a traditional tool of Kalman Filters can be successfuly applied to do this task. We study two mean-reverting log-spot price models and the Pilipovic model using correspondingly Kalman Filter the extended Kalman Filter. The results of applying this method to market data from several power exchanges are discussed.

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