Research On Sensorless Direct Torque Control Of Induction Machine Drives Based On Kalman Filter

Because of several advantages, simple structure, stable performance, low cost and easy manufacturing, Induction Machines(IMs) are attracting extensive attention in theory research and practical application. IM and its control system, has also been used in electric vehicles,transportation,CNC and other fields. The Direct Torque Control(DTC) technique and Field Oriented Control(FOC) technique, which used as high performance variable-frequency adjusting-speed technique for IM in the AC drives has been both researched. Especially,DTC is very popular to the scientific research institution because of its simplified control strategy,lower parameter dependence and fast behavior. However,Bang-Bang control mode of conventional DTC causes high ripples in stator flux, current and electromagnetic torque, accompanied by acoustical noise,which has become its inherent disadvantages and confined the DTC technique to be applied widely. It is best method to solve the above questions that a novel is founded by synthesizing vector control. Taking the sensorless IM control system as the research target, this dissertation conducted some research in-depth on the following issues, including on-line estimation of rotor speed and stator flux, IM parameter identification and so on. Its aims to reduce the cost and complexity of drive system, and further strengthen the reliability of DTC control system of IMs.In this paper,IM state equations of ω-Is-Ψs and ω-Is-Ψr in the two-phase stationary frame system were derived and a new Direct Torque Control strategy based on Space-Vector Modulation(DTC-SVM) is built firstly, and then an Extended Kalman Filter(EKF) for speed and stator flux estimation of IMs was designed based on the above work. Considering that the random parameters of IMs had a great impact on estimation performance, the influence on estimates by random parameters of IMs is analyzed by simulation results, and the disadvantage of conventional EKF is summarized.One problem associated with EKF is that suffers from computational burden,especially,for the IM which has a high order model. To overcome this drawback, a two-stage extended Kalman filter(TSEKF) is presented in this paper. Following the two-stage filtering technique, the TSEKF can be decomposed into two filters such as the full-order kalmam filter and the augmented kalman filter. Compared to the conventional EKF, the main advantage of the TSEKF is the ability to reduce the computational complexity, whilst maintaining the same level of performance.For the problem of estimation accuracy is easily impact by parameters variations, an in-depth study on strong tracking fading extended Kalman filter(SFEKF) is made and a SFEKF with matrix forgetting factor is designed based on innovation information for improving the estimation accuracy and tracking capability. To reduce computational complexity of SFEKF with matrix forgetting factor, a two stage strong tracking fading extended Kalman filter(STFEKF) which can be decomposed into full-order strong tracking fading kalmam filter and augmented strong tracking fading kalman filter is developed by the same method as in TSEKF. By inductive reasoning that theSTFEKF is equivalent to SFEKF has been demonstrated.When using a EKF,a good knowledge of electric parameters of IM is a precondition. So to study the offline parameters identification of IMs at standstill in depth is necessary. In this dissertation, a recursive least squares(RLS) algorithm is adopted. Based on linear regression RLS estimation model of IM, the resistance and inductance of IMs is identified and then the self-commissioning of the EKF model has been completed.Based on Expert3 system, a full-digital sensorless DTC system of IM is designed to realize the above algorithms. Based on these algorithms and hardware, in-depth simulation research and experiment validation are made.