Identification of VSD System Parameters with Particle Swarm Optimization Method

被引:0
|
作者
Qiu, Yiming [1 ]
Li, Wenqi [1 ]
Yang, Dongsheng [1 ]
Wang, Lei [1 ]
Wu, Qidi [1 ]
机构
[1] Tongji Univ, Sch Elect & Informat Engn, Shanghai 200092, Peoples R China
来源
关键词
PSO; VSD; Induction Motor; Parameter Identification;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A VSD system, which consists of an inverter & an induction motor, is now widely used in all kinds of application. But from the view point of an end user, neither the motor parameters in the mathematics model nor the vector controller structure are known. In this paper a PSO algorithm is programmed with IEC61131-3 language to estimate the parameters for the VSD system, based on the hardware of a vector controlled inverter, in order to reach the similar dynamic performance as a DC motor system. The PSO algorithm could be a kind of alternative approach of present parameter identification functions, for its requirements on the speed of CPU and volume of memory are low, while it converges quickly. It's especially helpful for the adjustment of complicated control system, when the technical requirements are clear & measurable.
引用
收藏
页码:227 / 233
页数:7
相关论文
共 50 条
  • [31] Particle swarm optimization with adaptive parameters
    Yang, Dongyong
    Chen, Jinyin
    Matsumoto, Naofumi
    SNPD 2007: EIGHTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING, AND PARALLEL/DISTRIBUTED COMPUTING, VOL 1, PROCEEDINGS, 2007, : 616 - +
  • [32] Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization
    DUAN HaiBin
    YU YaXiang
    ZHAO ZhenYu
    Science China(Information Sciences), 2013, 56 (01) : 121 - 132
  • [33] Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization
    HaiBin Duan
    YaXiang Yu
    ZhenYu Zhao
    Science China Information Sciences, 2013, 56 : 1 - 12
  • [34] Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization
    Duan HaiBin
    Yu YaXiang
    Zhao ZhenYu
    SCIENCE CHINA-INFORMATION SCIENCES, 2013, 56 (01) : 1 - 12
  • [35] Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization
    Ahmed, Troudi
    Mohamed, Bouzbida
    Abdelkader, Chaari
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2015, 23 (05) : 667 - 683
  • [36] A Novel Inertia Identification Method for PMSM Servo System Based on Improved Particle Swarm Optimization
    Wang, Shuo
    Yu, Dong
    Wang, Zhicheng
    2017 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2017), VOL 2, 2017, : 123 - 126
  • [37] Application of adaptive particle swarm optimization algorithm in system identification and parameter optimization
    Li, Xiaobin
    Kou, Demin
    Yu, Bo
    Jiang, Yun
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2007, 28 (SUPPL. 5): : 341 - 345
  • [38] An Extraction Method of Solar Cell Parameters with Improved Particle Swarm Optimization
    Ye, Meiying
    Zeng, Siqin
    Xu, Yousheng
    CHINA SEMICONDUCTOR TECHNOLOGY INTERNATIONAL CONFERENCE 2010 (CSTIC 2010), 2010, 27 (01): : 1099 - 1104
  • [39] Identification of strategy parameters for particle swarm optimizer through Taguchi method
    KHOSLA Arun
    KUMAR Shakti
    AGGARWAL K.K.
    Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2006, (12) : 1989 - 1994
  • [40] Identification of strategy parameters for particle swarm optimizer through Taguchi method
    Khosla A.
    Kumar S.
    Aggarwal K.K.
    Journal of Zhejiang University-SCIENCE A, 2006, 7 (12): : 1989 - 1994