On-Line Estimation of SPSG Parameters using Discrete Kalman Filters

被引：0
|
作者：
Larakeb, M. [1 ]
Bentounsi, A. [1 ]
Djeghloud, H. [1 ]
Rachid, A. [2 ]
机构：
[1] Technol Sci Fac, Lab Elect Engn Constantine, Constantine, Algeria
[2] Technol Sci Fac, MoDERNa Lab, Constantine, Algeria
来源：
2016 IEEE 16TH INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING (EEEIC) | 2016年
关键词：
SPSG; Kalman filter; disrete parametric estimation; bias; comparisons; OBSERVER;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Diverse estimators are available for on-line parametric identification; among them the discrete Kalman filter (DKF) is the most popular. It can be used in its traditional form (DTKF) for linear systems or in its extended form (DEKF) when the system is nonlinear. Another interesting application of the discrete Kalman filter is when it is biased (DBKF). The consideration of the bias makes it possible to reduce the mean squared error (MSE) between measured and estimated values of the system state variable. Therefore the normalized MSE (NMSE) can be diminished as well. Likewise, standard deviation (STD) between real and estimated values of the parameter can be limited in the tolerable percentage. All these situations are discussed in this paper where the system under study is a salient-pole synchronous generator (SPSG). MATLAB codes and Simulink models are implemented to validate the different DKFs. Finally comparative study between continuous and discrete KFs is provided and which reveals the benefit of the DKF.
引用
收藏
页数：6
相关论文
共 50 条
  • [1] On-Line Parameters Estimation of Low Scale SPSG Using Discrete Kalman Filters
    Larakeb, M.
    Bentounsi, A.
    Djeghloud, H.
    JOURNAL OF ELECTRICAL SYSTEMS, 2016, 12 (04) : 770 - 785
  • [2] Biased Kalman Filter Applied for On-Line Estimation of SPSG Parameters
    Larakeb, M.
    Bentounsi, A.
    Djeghloud, H.
    2016 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND CONTROL (ICSC), 2016, : 60 - 66
  • [3] Optimal on-line parameter estimation for a class of infinite dimensional systems using Kalman filters
    Demetriou, MA
    Ito, K
    PROCEEDINGS OF THE 2003 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2003, : 2708 - 2713
  • [4] On-line learning in recurrent neural networks using nonlinear Kalman filters
    Todorovic, B
    Stankovic, M
    Moraga, C
    PROCEEDINGS OF THE 3RD IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, 2003, : 802 - 805
  • [5] On-line Estimation of Transmission Line Parameters Using Synchronized Measurements
    Al-Othman, Abdulrahman K.
    El-Naggar, Khaled M.
    AlSharidah, Michel E.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2016, 44 (02) : 233 - 239
  • [6] Analysis of a Kalman filter based method for on-line estimation of atmospheric dispersion parameters using radiation monitoring data
    Drews, M
    Lauritzen, B
    Madsen, H
    RADIATION PROTECTION DOSIMETRY, 2005, 113 (01) : 75 - 89
  • [7] Methodology for the Estimation of Real Time Parameters using Kalman Filters and Least Squares
    Ortiz Bravo, V-Ctor Alfonso
    Nieto Arias, Manuel Antonio
    Quintero Salazar, Edwin Andre
    REVISTA ITECKNE, 2013, 10 (01): : 37 - 44
  • [8] An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model
    Zhang, Xu
    Wang, Yujie
    Yang, Duo
    Chen, Zonghai
    ENERGY, 2016, 115 : 219 - 229
  • [9] On-line estimation of suspended solids in biological reactors of WWTPs using a Kalman observer
    Beltran, S.
    Irizar, I.
    Monclus, H.
    Rodriguez-Roda, I.
    Ayesa, E.
    WATER SCIENCE AND TECHNOLOGY, 2009, 60 (03) : 567 - 574
  • [10] On-line estimation of synchronous generator parameters using PRBS perturbations
    Vermeulen, HJ
    Strauss, JM
    Shikoana, V
    2002 IEEE POWER ENGINEERING SOCIETY SUMMER MEETING, VOLS 1-3, CONFERENCE PROCEEDINGS, 2002, : 996 - 996
12345