Particle swarm optimization of an extended Kalman filter for speed and rotor flux estimation of an induction motor drive

被引：0
作者：
Yahia Laamari
Kheireddine Chafaa
Belkacem Athamena
机构：
[1] Annaba University,Electronics Department, Faculty of Technology
[2] M’Sila University,Electrical Engineering Laboratory
[3] Batna University,Electronics Department, Faculty of Technology
[4] Al Ain University of Science and Technology,undefined
来源：
Electrical Engineering | 2015年 / 97卷
关键词：
Induction motors; Speed estimation; Stochastic state observer; Extended Kalman filter; Particle swarm optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A novel method based on a combination of the extended Kalman filter with particle swarm optimization (PSO) to estimate the speed and rotor flux of an induction motor drive is presented. The proposed method will be performed in two steps. As a first step, the covariance matrices of state noise and measurement noise will be optimized in an off-line manner by the PSO algorithm. As a second step, the optimal values of the above covariance matrices are injected in our speed–rotor flux estimation loop (on-line). Computer simulations of the speed and rotor flux estimation have been performed to investigate the effectiveness of the proposed method. Simulations and comparison with genetic algorithms show that the results are very encouraging and achieve good performances.
引用
收藏
页码：129 / 138
页数：9
相关论文
共 41 条
[1]  
Aydogmus O(2012)Implementation of EKF based sensorless drive system using controlled PMSM fed by a matrix converter Electr Power Energy Syst 43 736-743
[2]  
Sunter S(2006)Simple derivative-free nonlinear state observer for sensorless AC drives IEEE/ASME Trans Mechatron 11 634-642
[3]  
Akin B(1971)An introduction to observers IEEE Trans Autom Control 16 596-602
[4]  
Orguner U(2005)Speed sensorless stator flux-oriented control of induction motor in the field weakening region using Luenberger observer IEEE Trans Power Electron 20 864-969
[5]  
Ersak A(1960)A new approach to linear filtering and prediction problems Trans ASME J Basic Eng 82 35-45
[6]  
Ehsani DG(2007)Fuzzy modelling using Kalman filter IET Control Theory Appl 1 58-64
[7]  
Luenberger TS(1989)Application of extended Luenberger observer for flux and rotor time-constant estimation in induction motor drives IEE Proc Control Theory Appl 136 324-330
[8]  
Kwon MH(2010)MRAS sensorless vector control of an induction motor using new sliding-mode and fuzzy-logic adaptation mechanisms IEEE Trans Energy Convers 25 394-402
[9]  
Shin DS(2002)Comparative study of a sliding-mode observer and Kalman filters for full state estimation in an induction machine IEE Proc Electr Power Appl 149 53-64
[10]  
Hyun RE(2002)Speed estimation of an induction motor drive using an optimized extended Kalman filter IEEE Trans Ind Electron 49 124-133
12345