A MULTIPLE-MODEL APPROACH FOR SYNCHRONOUS GENERATOR NONLINEAR SYSTEM IDENTIFICATION

被引:2
|
作者
Ahmadi, Seyed Salman [1 ]
Karrari, Mehdi [1 ]
机构
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran
关键词
multiple model approach; power system identification; power system modeling; synchronous generator; MACHINE PARAMETER-ESTIMATION;
D O I
10.2478/v10187-012-0035-5
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a multiple model approach is proposed for the identification of synchronous generators. In the literature, the same structure often is used for all local models. Therefore, to obtain a precise model for the operating condition of the synchronous generator with severely nonlinear behavior, many local models are required. The proposed method determines the complexity of local models based on complexity of behavior of the synchronous generator at different operating conditions. There are two choices for increasing model precision at each iteration of the proposed method: (i) increasing the number of local models in one region, or (ii) increasing local model complexity in the same region. The proposed method has been tested on experimental data collected on a 3 kVA micro-machine. In the study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous generator. The proposed method provides a more precise model with fewer parameters compared to some well known methods such as LOLIMOT and global polynomial models.
引用
收藏
页码:249 / 254
页数:6
相关论文
共 50 条
  • [1] Nonlinear system identification: From multiple-model networks to Gaussian processes
    Gregorcic, Gregor
    Lightbody, Gordon
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2008, 21 (07) : 1035 - 1055
  • [2] Robust multiple-model LPV approach to nonlinear process identification using mixture t distributions
    Lu, Yaojie
    Huang, Biao
    [J]. JOURNAL OF PROCESS CONTROL, 2014, 24 (09) : 1472 - 1488
  • [3] INSTRUMENTAL ENVIRONMENT FOR HYDRO GENERATOR NONLINEAR MODELING AND MULTIPLE-MODEL ADAPTIVE CONTROL
    Ruzhekov, Georgi
    Puleva, Teofana
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION 2010 IN PRAGUE (MS'10 PRAGUE), 2010, : 383 - 386
  • [4] Nonlinear System Identification With Robust Multiple Model Approach
    Liu, Xin
    Yang, Xianqiang
    Yin, Shen
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2020, 28 (06) : 2728 - 2735
  • [5] LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution
    Yu, Miao
    Yang, Xianqiang
    Liu, Xinpeng
    [J]. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2021, 52 (07) : 1452 - 1465
  • [6] Parameters identification of nonlinear state space model of synchronous generator
    Kou, Pangao
    Zhou, Jianzhong
    Wang, Changqing
    Xiao, Han
    Zhang, Huifeng
    Li, Chaoshun
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2011, 24 (07) : 1227 - 1237
  • [7] Synchronous generator nonlinear model identification using Wiener-Neural model
    Ghomi, M.
    Sarem, Y. Najafi
    Kermajani, H. R.
    Poshtan, J.
    [J]. 2007 42ND INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE, VOLS 1-3, 2007, : 236 - 241
  • [8] DEKF SYSTEM FRO CROWDING ESTIMATION BY A MULTIPLE-MODEL APPROACH
    CRAVINO, F
    DELLUCCA, M
    TESEI, A
    [J]. ELECTRONICS LETTERS, 1994, 30 (05) : 390 - 391
  • [9] Multiple-model adaptive explicit predictive control for nonlinear MIMO system
    Dutta, Lakshmi
    Das, Dushmanta Kumar
    [J]. JOURNAL OF CONTROL AND DECISION, 2024,
  • [10] Nonlinear, Magnetics Model of a Synchronous Generator
    Woodburn, David
    Wu, Thomas
    Camarano, Anthony
    [J]. 2015 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE (IEMDC), 2015, : 1614 - 1620