基于感应电动机复数简化模型的参数辨识研究
CSTR:
作者:
作者单位:

作者简介:

姜 杰(1958-),男,副教授,主要从事电力系统保护与综合自动化研究;

通讯作者:

中图分类号:

基金项目:


Research on identification of induction motor based on its simplified complex quantity models
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    感应电动机数学模型的非线性、强耦合和模型中不可观测量的存在,使得在辨识电机参数时需要求解复杂的非线性微分方程组而导致辨识困难。提出一种由定子电压和电流交直轴分量表示的感应电动机复数简化模型;利用端口电流电压暂态量,采用步长加速法进行模型参数辨识。该模型考虑了电动机的非线性,避免了求解复杂的非线性微分方程组,减化了参数辨识过程,保证了算法的收敛性和结果的准确性,提高了辨识的稳定性和效率。在两种电机控制方式下进行算例仿真及参数辨识,验证表明该模型正确有效,参数可辨识且精度高,易于工程实现。

    Abstract:

    Because of nonlinear, strong coupling and immeasurable variables, induction motor parameter identification need solve complex nonlinear differential equations; it may lead to difficulties in the parameters identification process. A simplified complex-quantity model, which is expressed by straight axis and quadrature axis component of the voltage and current, is proposed for induction motor. Using step acceleration method, the parameters of the model are identified according to voltage and current transient data. This model simplifies the process of parameter identification by taking nonlinear characteristic of induction motor into account and avoiding solving the complex nonlinear differential equations. So the convergence of the algorithm and the accuracy of the results are ensured and the stability and efficiency of the identification process are improved. The examples are conducted under two kinds of motor control methods, and the simulation results show identifiability and high precision, which verify the correctness, effectiveness and practice of the model.

    参考文献
    相似文献
    引证文献
引用本文

姜杰,王学斌,殷家敏,等.基于感应电动机复数简化模型的参数辨识研究[J].电力系统保护与控制,2014,42(19):87-92.[JIANG Jie, WANG Xue-bin, YIN Jia-min, et al. Research on identification of induction motor based on its simplified complex quantity models[J]. Power System Protection and Control,2014,V42(19):87-92]

复制
分享
相关视频

文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2013-12-30
  • 最后修改日期:2014-03-15
  • 录用日期:
  • 在线发布日期: 2014-09-26
  • 出版日期:
文章二维码