Research On Flux Observer And Parameter Identification Of Induction Motor

The AC drives have played a dominant place in the field of variable speed drives and various high-performance variable frequency AC drives are widely applied in industrial and agricultural production. Flux estimation and parameters identification are the critical technologies in high-performance AC drives, and have turned to be the hotspot of research. AC three-phase induction motors are strongly coupling high-order systems of multiple variables and nonlinearity. This paper summarized the complex number models of AC motors based on space vector analysis, which have the features of clear physical concepts and concise forms. In this paper, it proposed the universal form of flux observer of stator and rotor based on Luenberger state-observer theory, and it improved the robustness of flux observer by the selection of feedback matrix, which makes certain parameters not appear in models of flux observer so that it are not influenced by the parameters. It is verified that the common flux models are just the special cases of Luenberger flux observer.One kind of model reference approach for adaptive flux estimation and parameter identification are presented, which can not only observe the stator or rotor flux accurately, but also identify all the four parameters rapidly in the case of persistent excitation. When some parameters are known, the rest of the parameters could also be identified even though the excitation is not persistent. The paper has studied this approach in stator and rotor reference frame respectively, deducted the adaptive laws of parameters and theoretically proved the astringency of flux and parameters in certain conditions. Simulation by Matlab/Simulink and then experiments with sampled data from an induction motors have been done, and the results of simulation and experiments show that the method are accurate and effective.Genetic algorithm is a kind of well-rounded global optimization method that owns the features of strong robustness and broad applicability. As the genetic algorithm is not limited by character of the problems, it has deep potential in dealing with parameters identification of motors. This paper proposed the parameters identification based on genetic algorithm using data of induction motors during starting process. It is showed that in the stator reference frame, the stator resistance and whole leaking inductance have relatively high identification accuracy while therotor time constant and stator inductance have low identification accuracy, and in rotor reference frame, the leaking inductance, rotor time constant and stator inductance have high identification accuracy, but the identification accuracy of stator resistance has obvious decrease. If the identification in two reference frames are combined together, the stator resistance is identified in stator reference frame in advance, and then other parameters are gained in rotor reference frame;the identification accuracy of all the parameters will be improved a lot.With regard to the rotor-field-orientated vector control system, the change of rotor time constant has the greatest influence on the running performance of system compared to other parameters. So in this paper, it proposed another new method for adjusting the time constant of induction motors online, which intermittently overlap narrow negative pulse signal into the flux reference current, and then find out whether vector control was rotor-flux-oriented accurately according to the change of torque caused by overlapped pulse, and hereby adjust the time constant correspondingly. The simulation results showed this method could track the variance of rotor time constant very well.