Fuzzy and Neural Rotor Resistance Estimator for Vector Controlled Induction Motor Drives

This paper contributes to improving the dynamic performance of indirect vector controlled induction motor drives. This command requires the rotor resistance; the variation of this parameter could distort the decoupling between the flux and torque and, consequently, lead to deterioration of performance. To overcome this problem two intelligent approaches have been introduced to estimate the rotor resistance namely fuzzy logic and artificial neural networks. These estimators process the information from the rotational speed, the stator currents and voltages. The performances of the two intelligent approaches are investigated and compared in simulation. The results show that the neural rotor resistance estimator is reliable and highly effective in the resistance identification relative to fuzzy rotor resistance estimator of induction motor drives.

[1]  E. Levi,et al.  A Review of RFO Induction Motor Parameter Estimation Techniques , 2002, IEEE Power Engineering Review.

[2]  Demba Diallo,et al.  A Fuzzy Rotor Resistance Updating Scheme for an IFOC Induction Motor Drive , 2001 .

[3]  Mohamed Benbouzid,et al.  2001 Middle-East Power Systems Conference 29-31 December 2001 Helwan, Cairo, Egypt , 2001 .

[4]  Mondher Farza,et al.  High gain observer based on-line rotor and stator resistances estimation for IMs , 2010, 2010 IEEE International Conference on Control Applications.

[5]  Pierluigi Pisu,et al.  Simplified exponentially convergent rotor resistance estimation for induction motors , 2003, IEEE Trans. Autom. Control..

[6]  David J. Livingstone,et al.  Artificial Neural Networks , 2009 .

[7]  Hamid A. Toliyat,et al.  Neural-Network-Based Parameter Estimations of Induction Motors , 2008, IEEE Transactions on Industrial Electronics.

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Shyi-Ming Chen,et al.  A fuzzy reasoning approach for rule-based systems based on fuzzy logics , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Mondher Farza,et al.  High Gain Observer based On-line Rotor and Stator Resistances Estimation for IMs , 2010, CCA.

[11]  H. Madadi Kojabadi.  Active power and MRAS based rotor resistance identification of an IM drive , 2009, Simul. Model. Pract. Theory.

[12]  Scott D. Sudhoff,et al.  Analysis of Electric Machinery and Drive Systems , 1995 .

[13]  Werner Leonhard,et al.  Control of Electrical Drives , 1990 .

[14]  Samir Kouro,et al.  Unidimensional Modulation Technique for Cascaded Multilevel Converters , 2009, IEEE Transactions on Industrial Electronics.

[15]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[16]  F.R. Salmasi,et al.  Improved adaptive feedback linearization control of induction motors based on online estimation of core loss and rotor resistance , 2006, International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 2006. SPEEDAM 2006..