Identification of Asynchronous Machine Parameters by Evolutionary Techniques

Four evolutionary techniques (scatter search, evolutionary programming, ant colony, and particle swarm algorithms) were used for off-line identification of three phase asynchronous machine parameters. Optimization techniques were then tested on two distinct machines. In order to evaluate how much good the achieved machines parameters obtained, experimental and simulation input-output behaviors are presented for each method. The performances in term of objective function and convergence time prove the effectiveness of this class of optimization methods.

[1]  Ali Keyhani,et al.  Nonlinear neural-network modeling of an induction machine , 1999, IEEE Trans. Control. Syst. Technol..

[2]  Vladimir Cretu,et al.  Testing of electrical machines using a data acquisition and processing system , 2001 .

[3]  Fernando Ramos,et al.  Evolving Insect Locomotion Using Cooperative Genetic Programming , 2000, MICAI.

[4]  A. Rezzoug,et al.  The Hooke and Jeeves algorithm approach in the identification of the induction machines parameters , 2000 .

[5]  A. S. Bharadwaj,et al.  A review of parameter sensitivity and adaptation in indirect vector controlled induction motor drive systems , 1990, 21st Annual IEEE Conference on Power Electronics Specialists.

[6]  A. Keyhani,et al.  Induction motor parameter identification from operating data for electric drive applications , 1999, Gateway to the New Millennium. 18th Digital Avionics Systems Conference. Proceedings (Cat. No.99CH37033).

[7]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  D. S. Zinger,et al.  PI and fuzzy estimators for tuning the stator resistance in direct torque control of induction machines , 1994 .

[9]  Michel Poloujadoff,et al.  Modelling and Identification of Parameters of Saturated Induction Machine Operating Under Motor and Generator Conditions , 1999 .

[10]  F. Alonge,et al.  Parameter Identification of Induction Motors: Least Squares vs. Genetic Algorithms , 1998 .

[11]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[12]  Bimal K. Bose,et al.  Quasi-fuzzy estimation of stator resistance of induction motor , 1998 .

[13]  Jennifer Stephan,et al.  Real-time estimation of the parameters and fluxes of induction motors , 1992, Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting.

[14]  Kyo-Beum Lee,et al.  Disturbance observer that uses radial basis function networks for the low speed control of a servo motor , 2005 .

[15]  A. Keyhani,et al.  Identification of Variable Frequency Induction Motor Models from Operating Data , 2002, IEEE Power Engineering Review.

[16]  Paul C. Krause,et al.  Analysis of electric machinery , 1987 .

[17]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[18]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Peter Idowu Robust Neural Net-Based Inverse-Model Identification of an Induction Motor , 1999 .

[20]  Guanghu Xu,et al.  Analyzing the Influence of Induction Motor Inertia on Power System Low Frequency Oscillation , 2005 .