Parameter Identification of a Permanent Magnet Synchronous Machine based on Current Decay Test and Particle Swarm Optimization

被引:1
|
作者
Perez, J. N. H. [1 ]
Hernandez, O. S. [2 ]
Caporal, R. M. [1 ]
Magdaleno, J. de J. R. [3 ]
Barreto, H. P. [4 ]
机构
[1] Inst Tecnol Apizaco, Tlaxcala, Mexico
[2] Inst Nacl Astrofis Opt & Electr, Puebla, Mexico
[3] Inst Nacl Astrofis Opt & Electr, Dept Elect, Puebla, Mexico
[4] Lab Invest Control Reconfigurable, Queretaro, Mexico
关键词
Digital signal processor; parameter identification; permanent magnet synchronous machine; PSO;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Permanent Magnet Synchronous Machine (PMSM) is widely used in industrial applications. In order to obtain a high performance operation, an accurate knowledge of the machine parameters, such as direct (d) and quadrature (q) stator inductances is necessary. This paper presents two different methods to identify the stator inductances taking into account the magnetic saturation. First, Current Decay Test (CDT) is presented, then, Particle Swarm Optimization (PSO) algorithm. Both methods are used to identify the stator inductances. An own power electronic drive and a low cost digital signal processor (DSP) have been used on the experimental setup. Experimental results are presented to validate the theoretical work.
引用
收藏
页码:1176 / 1181
页数:6
相关论文
共 50 条
  • [2] Real-time particle swarm optimization based parameter identification applied to permanent magnet synchronous machine
    Liu, Wenxin
    Liu, Li
    Chung, Il-Yop
    Cartes, David A.
    APPLIED SOFT COMPUTING, 2011, 11 (02) : 2556 - 2564
  • [3] Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor
    Zhou, Shuai
    Wang, Dazhi
    Ni, Yongliang
    Song, Keling
    Li, Yanming
    CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 79 (02): : 2187 - 2207
  • [4] Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors
    Liu, Li
    Liu, Wenxin
    Cartes, David A.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2008, 21 (07) : 1092 - 1100
  • [5] Parameter Identification of Permanent Magnet Synchronous Machine based on Metaheuristic Optimization
    Balamurali, Aiswarya
    Mollaeian, Aida
    Sangdehi, Seyed Mousavi
    Kar, Narayan C.
    2015 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE (IEMDC), 2015, : 1729 - 1734
  • [6] Parameter identification of permanent magnet synchronous motor based on modified- fuzzy particle swarm optimization
    Zhou, Shuai
    Wang, Dazhi
    Li, Ye
    ENERGY REPORTS, 2023, 9 : 873 - 879
  • [7] Multi-parameter identification of permanent magnet synchronous motor based on improved particle swarm optimization
    Liu X.-P.
    Hu W.-P.
    Zou Y.-L.
    Zhang Y.
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2020, 24 (07): : 112 - 120
  • [8] Parameter identification of permanent magnet synchronous motor based on modified- fuzzy particle swarm optimization
    Zhou, Shuai
    Wang, Dazhi
    ENERGY REPORTS, 2023, 9 : 873 - 879
  • [9] Iterative particle swarm optimization based parameter identification of lumped-parameter thermal network for permanent magnet synchronous motors
    Meng, Zhijin
    Liu, Yuyang
    Chen, Li
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2024, 28 (01): : 1 - 11
  • [10] Parameter identification of permanent magnet synchronous motors using quasi-opposition-based particle swarm optimization and hybrid chaotic particle swarm optimization algorithms
    Ahandani, Morteza Alinia
    Abbasfam, Jafar
    Kharrati, Hamed
    APPLIED INTELLIGENCE, 2022, 52 (11) : 13082 - 13096