吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (06): 1596-1600.doi: 10.7964/jdxbgxb201306026

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Off-line parameter identification for induction motor based on reconstructed voltage

ZHAO Fei-xiang1, ZHANG Jian-wei1, GUO Kong-hui1, ZHANG Li-hao1, ZHENG Zhong2   

  1. 1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
    2. College of Automotive Engineering, Jilin University, Changchun 130022, China
  • Received:2012-11-21 Online:2013-11-01 Published:2013-11-01

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Abstract:

Vector control schemes for induction motor drive systems have been widely adopted in high performance applications. Vector control transforms the control of an induction motor to that of separately excited dc motor by creating independent channels for flux and torque control. Crucial to the success of a vector control scheme is the knowledge of the instantaneous position of the rotor flux. The position of the rotor flux is estimated in the direct vector control scheme, which requires a priori knowledge of the machine parameters. Identification of machine parameters is a basic step in designing a motor controller. In traditional method, voltage required for off-line identification of induction motor parameters is commonly obtained by filter circuit and data acquisition equipment, and the equivalent circuit of the induction motor is usually modified. The reason that traditional off-line identification result of induction motor parameters is not suitable to online model is analyzed. The internal resistance of the power devices is equivalised to the stator resistance of the motor first. Then the voltage distortion caused by dead-time is eliminated through offline voltage correction. Finally, a genetic algorithm is used to identify the equivalent electrical parameters of induction motor. Experiment results show that the parameters obtained by this method can accurately describe the steady-state and transient response of the induction motor under specific inverter power supplying conditions.

Key words: electrical engineering, induction motor, parameter identification, reconstructed voltage, distortion correction, genetic algorithm

CLC Number: 

  • TM343

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