Determination of induction motor parameters with differential evolution algorithm

被引:0
作者
Mustafa Arslan
Mehmet Çunkaş
Tahir Sağ
机构
[1] Selcuk University,Technical Sciences Vocational School, Electrical Department
[2] Selcuk University,Faculty of Technical Education, Electronics and Computer Education
来源
Neural Computing and Applications | 2012年 / 21卷
关键词
Inductions motor; Parameter determination; Differential evolution algorithm; Genetic algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
In this study, the determination of equivalent circuit parameters of induction motors is carried out with differential evolution algorithm (DEA) and genetic algorithm (GA). As an objective function in the algorithms, the sum torque error at zero speed, pull-out, and rated speed is used. The determination of equivalent circuit parameters is performed with three induction motors of 2.2, 5.5, and 37 kW. In particular, the search ability of DEA is compared with GA by using the same population size, number of iteration, and crossover rate. In addition, the effects of the obtained equivalent circuit parameters on induction motors characteristics are investigated and presented with graphics. The results show that the use of DEA instead of GA increases the convergence sensitivity and reduces the simulation time.
引用
收藏
页码:1995 / 2004
页数:9
相关论文
共 34 条
  • [1] Arabacı H(2010)Automatic detection and classification of rotor cage faults in squirrel cage induction motor Neural Comput Applic 19 713-723
  • [2] Bilgin O(1991)Observers for induction motor state and parameter estimation IEEE Trans Ind Appl 27 1119-1127
  • [3] Atkinson DJ(2002)Identification of variable frequency induction motor models from operating data IEEE Trans Energy Conv 17 24-31
  • [4] Acarnley PP(2001)Least squares and genetic algorithms for parameter identification of induction motors Control Eng Pract 9 647-657
  • [5] Finch JW(1995)Identification and control of induction machines using arificial neural networks IEEE Trans Ind Appl 31 612-619
  • [6] Proca AB(2005)Parameter identification of induction motors using dynamic encoding algorithm for searches (DEAS) IEEE Trans Energy Conv 20 16-24
  • [7] Keyhani A(2006)Identification of asynchronous machine parameters by evolutionary techniques Electric Power Components Syst 34 1359-1376
  • [8] Alonge F(2009)Soft computing identification techniques of asynchronous machine parameters: evolutionary. Strategy and chemotaxis algorithm Turk J Elec Eng Comp Sci 17 69-85
  • [9] D’Ippolito F(2008)Parameter estimation of induction machines from nameplate data using particle swarm optimization and genetic algorithm techniques Electric Power Components Syst 36 801-814
  • [10] Raimondi FM(2010)Intelligent design of induction motors by multiobjective fuzzy genetic algorithm J Intell Manuf 21 393-402