Synchronous Generator Dual Estimation Using Sigma Points Kalman Filter

被引:1
作者
Zoghi, M. [1 ]
Yaghobi, H. [1 ]
机构
[1] Semnan Univ, Fac Elect & Comp Engn, Semnan, Iran
来源
INTERNATIONAL JOURNAL OF ENGINEERING | 2024年 / 37卷 / 07期
关键词
Synchronous Generator; Kalman Filter; Centeral Diffrence Kalman Filter; Estimation; PARAMETERS; TRACKING; STATE;
D O I
10.5829/ije.2024.37.07a.04
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this article, the central difference Kalman filter (CDKF) has been used to estimate the parameters of two different models of synchronous generator (SG) in the presence of noise. It should be mentioned that there are different models of synchronous generators with different levels of accuracy for use in estimation algorithms. The estimation algorithm in this paper uses a smaller number of measurement inputs to estimate the states and unknown parameters for two exact models of the synchronous generator. The central difference Kalman filter (CDKF) is a member of the Kalman filter family, which, like the unscented Kalman filter (UKF), uses sigma points to model nonlinear equations. The differential Kalman filter (CDKF) provides better results than the unscented Kalman filter. In this research, by using two synchronous generator models with different parameters in three scenarios, the ability of the Kalman filter of the central difference is challenged, which shows that this method is very efficient and reliable.
引用
收藏
页码:1239 / 1251
页数:13
相关论文
共 50 条
[41]   Using the Kalman filter for parameter estimation in biogeochemical models [J].
Trudinger, C. M. ;
Raupach, M. R. ;
Rayner, P. J. ;
Enting, I. G. .
ENVIRONMETRICS, 2008, 19 (08) :849-870
[42]   Tire Lateral Force Estimation Using Kalman Filter [J].
Lee, Eunjae ;
Jung, Hojin ;
Choi, Seibum .
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2018, 19 (04) :669-676
[43]   MOTION ESTIMATION IN FLOTATION FROTH USING THE KALMAN FILTER [J].
Amankwah, Anthony ;
Aldrich, Chris .
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, :1897-1900
[44]   Modal parameter estimation using interacting Kalman filter [J].
Zghal, Meriem ;
Mevel, Laurent ;
Del Moral, Pierre .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2014, 47 (1-2) :139-150
[45]   Friction coefficient estimation using an unscented Kalman filter [J].
Zhao, Yunshi ;
Liang, Bo ;
Iwnicki, Simon .
VEHICLE SYSTEM DYNAMICS, 2014, 52 :220-234
[46]   Tire Lateral Force Estimation Using Kalman Filter [J].
Eunjae Lee ;
Hojin Jung ;
Seibum Choi .
International Journal of Automotive Technology, 2018, 19 :669-676
[47]   A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter [J].
Hong, Sanghyun ;
Lee, Chankyu ;
Borrelli, Francesco ;
Hedrick, J. Karl .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 16 (01) :151-161
[48]   Improved Cubature Kalman Filter for GNSS/INS Based on Transformation of Posterior Sigma-Points Error [J].
Cui, Bingbo ;
Chen, Xiyuan ;
Tang, Xinhua .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (11) :2975-2987
[49]   On the evaluation of uncertainties for state estimation with the Kalman filter [J].
Eichstaedt, S. ;
Makarava, N. ;
Elster, C. .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2016, 27 (12)
[50]   Improving the Performance of the MPPT for Thermoelectric Generator System by Using Kalman Filter [J].
Yahya, Khalid ;
Bilgin, Mehmet Zeki ;
Erfidan, Tarik ;
Cakir, Bekir .
2018 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONIC ENGINEERING (ICEEE), 2018, :129-132