Research On Novel State Estimation And Parameter Identification Methods Of Induction Motor

The AC motors have occupied a dominant position in the electrical power drive field based on the advantages of economy and technology. Various high performance AC speed adjustment technologies are widely researched and applied. The rotor field oriented control has brought essential advances in AC speed adjustment system. Speed sensorless control of induction motor (IM) promotes the simplicity and robustness further. There are two problems must be solved in this system: the speed estimation and rotor flux observation.Extended Kalman Filter (EKF) is an effective state estimation algorithm of IM. But it has two major defects: (1) bad robustness to the variation of motor parameters; (2) bad tracking ability to the abrupt change of states. To overcome these disadvantages, the Strong Track Filter (STF) is introduced to estimate the motor states, which can improve the estimation performance of abrupt change states and the robustness of variable parameters. Besides, the speed is considered as a constant in the traditional EKF-based estimator, which results to the bad estimation precision at very low and zero speed. In this paper, the mechanism and torque equations are introduced into the model of IM. Additionally, the speed is regarded as a variable, and the load torque is added to the state vector. It can improve the speed estimation precision and avoid the effects of lacking signal or friction at very low and zero speed to estimate the load torque.The state estimators based on full-order model of IM need high-order matrix operations, which has large computational burden. Therefor, the reduced-order model of IM is derived, while its observation equation is the first-order state delay, so its states can't be estimated by EKF directly. Therefore, the Schmidt Extended Kalman Filter (SEKF) is introduced. Since SEKF inherits the basic algorithm of EKF, it has the same disadvantages as EKF. Using the concept of STF to improve it, the Strong Track Schmidt Filter (STSF) is proposed, and is applied to speed estimation and flux observation of IM. The simulation and experiment results illustrate that the STSF-based state estimator of IM has satisfactory dynamic and static estimation performance, and it also has lower computational complexity. The parameters of IM are supposed as constant in the above proposed state estimation algorithms, but in fact they are time-varying along with the changes of working condition during the operation. Simulation results illuminate that the estimation precisions of EKF and STF are affected by the changes of parameters. In order to obtain the high performance of state estimation, the parameters must be online identified. Therefore, an STF-based identification method is proposed to estimate the stator and rotor resistance, which has satisfactory estimation precision. Since magnetic inductance is highly nonlinear, the algorithm will be complex using STF. Two novel magnetic inductance identification approaches are proposed, one is based on Unscented Kalman Filter (UKF), and the other is based on Dual Extended Kalman Filter (Dual EKF). Simulation and experiment results show that the proposed algorithms can identify the parameters of IM exactly, and avoid the state estimation impacted by parameter variety.In the above state estimation methods, the problem of parameter self-adaptive is solved by online identification. However, identification needs a process, the result of this cycle will be used at the next cycle, so the track of system model has delay essentially, and the dynamic performance is affected. Therefore, Multiple Model (MM) algorithm is introduced to estimate the states and parameters of IM. In order to increase estimation precision and decrease computational burden, a Single Filter Multiple Model (SFMM) algorithm is proposed. Combined with the variable structure method, a Single Filter Variable Structure Multiple Model (SFVSMM) algorithm is obtained. Simulation and experiment results illustrate that it has satisfactory estimation performance and proper computational burden.The proposed state and parameter estimation methods in this paper are experimented using the reciprocal power-fed AC drive test-bed. A combined state estimation method is proposed based on STSF, which can estimate the coaxial speed and load torque of two motors simultaneously and increase the precision efficiently. The parameter identification methods also have high precision, which can satisfy the request of high performance speed sensorless control of IM.