Parameters estimation of three-phase induction motors using differential evolution

被引:47
|
作者
Guedes, Jacqueline Jordan [1 ]
Castoldi, Marcelo Favoretto [1 ]
Goedtel, Alessandro [1 ]
Agulhari, Cristiano Marcos [1 ]
Sanches, Danilo Sipoli [1 ]
机构
[1] Univ Tecnol Fed Parana, BR-86300000 Cornelia Procopio, PR, Brazil
关键词
Induction motor; Differential evolution; Parameters estimation; ALGORITHM; IDENTIFICATION;
D O I
10.1016/j.epsr.2017.08.033
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
Three-phase induction motors are extensively used in the industry due to their robustness characteristics, low cost and easy maintenance. Usually, it is necessary to implement drive and control systems for such motors, which requires the knowledge of their mechanical and electrical parameters. However, in some cases, these data are not immediately available, or the values of the parameters may change due to the wear of motor components. Such problems can be circumvented if an efficient parameter estimation technique is available. In order to automatically estimate the parameters efficiently, the present work proposes a method, based on the differential evolution algorithm, aimed at the estimation of the electrical and mechanical parameters of three-phase induction motors. Such algorithm is capable of estimating the parameters of the equivalent electrical circuit, such as stator and rotor resistances and leakage inductances, the magnetizing inductance, and also mechanical parameters, such as moment of inertia and the friction coefficient. The performance of the proposed parameter estimation technique is evaluated for three different input signals: (i) current signal of a phase associated with the speed measured from a tachogenerator, (ii) current signal of a phase associated with the speed acquired from a torquemeter, and (iii) only the current signal of one phase. Finally, a series of simulated and experimental results are presented to validate the proposed technique, and the results show the good performance of the proposed strategies. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:204 / 212
页数:9
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