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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bo Zhang Qiongxuan Ge Qiankun Chang Shutian Zhang |
| Copyright Year | 2014 |
| Description | Author affiliation: Key Lab. of Power Electron. & Electr.Drive, Inst. of Electr. Eng., Beijing, China (Bo Zhang; Qiongxuan Ge; Qiankun Chang; Shutian Zhang) |
| Abstract | In sensorless induction motor drives, the stator model is always used to estimate the stator flux because it does not require a speed signal. Combined with the stator model, stator field orientation control strategies are preferred in implementation. In these strategies, direct torque and flux control based on space-vector modulation (SVM-DTC) is the one with good performance. It produces fast torque and flux control with constant switching frequency, and, compared with the conventional DTC, the ripples of torque, flux and current is reduced. However, the stator model suffers from the initial value and DC drift problems of pure integrator, which makes the SVM-DTC strategy not stable when the motor starts or runs in low-speed range. In this paper, a modified stator model is designed to solve the problems. In combination with a simple and practical speed estimation algorithm, a high-performance sensorless SVM-DTC strategy is constructed. The validity of the control strategy is verified through simulation and experiment results. |
| Starting Page | 1724 |
| Ending Page | 1729 |
| File Size | 2210911 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479943159 |
| DOI | 10.1109/ICIEA.2014.6931446 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Stators Induction motors Rotors Torque Estimation Band-pass filters Mathematical model stator flux estimation Induction motor speed sensorless SVM-DTC modified stator model |
| Content Type | Text |
| Resource Type | Article |
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