Title :
Identification of Induction Machine Electrical Parameters Using Genetic Algorithms Optimization
Author :
Kampisios, Konstantinos ; Zanchetta, Pericle ; Gerada, Chris ; Trentin, Andrew
Author_Institution :
Sch. of Electr. & Electron. Eng., Univ. Of Nottingham, Nottingham
Abstract :
This paper introduces a new heuristic approach for identifying induction motor equivalent circuit parameters based on experimental transient measurements from a vector controlled induction motor (I.M.) drive and using an off line genetic algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters is achieved by minimizing the error between experimental responses (speed or current) measured on a motor drive and the respective ones obtained by a simulation model based on the same control structure as the experimental rig, but with varying electrical parameters. An accurate and fast estimation of the electrical motor parameters is so achieved. Results are verified through a comparison of speed, torque and line current responses between the experimental IM drive and a Matlab-Simulink model.
Keywords :
genetic algorithms; induction motor drives; linear motors; machine vector control; parameter estimation; genetic algorithms optimization; induction machine electrical parameters; induction motor equivalent circuit parameters; linear machine model; parameter identification; simulation model; vector controlled induction motor drive; Current measurement; Electric variables measurement; Equivalent circuits; Error correction; Genetic algorithms; Induction machines; Induction motors; Mathematical model; Vectors; Velocity measurement;
Conference_Titel :
Industry Applications Society Annual Meeting, 2008. IAS '08. IEEE
Conference_Location :
Edmonton, Alta.
Print_ISBN :
978-1-4244-2278-4
Electronic_ISBN :
0197-2618
DOI :
10.1109/08IAS.2008.165