DocumentCode
156983
Title
Current-sensor-based predictive current control of a synchronous reluctance motor fed by a six-switch three-phase inverter
Author
Yuan-Ting Lo ; Cheng-Kai Lin ; Hsing-Cheng Yu ; Ming-Tsan Lin
Author_Institution
Dept. of Electr. Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan
fYear
2014
fDate
23-25 April 2014
Firstpage
1
Lastpage
4
Abstract
In this study, we propose a current-sensor-based predictive current control (CSBPCC) for six-switch three-phase (SSTP) inverter-fed synchronous reluctance motor (SynRM) drives. Generally speaking, two current sensors are used for detection of a- and b-phase stator currents of a SynRM drive system, respectively. In each sampling interval, the stator current will be read once rather than twice. After that, the past current variation can be saved and classified according to different switching states generated by the inverter. Next, the sampled stator current and the previously saved current variations are used to predict the future stator current without calculating the back-EMF or any motor´s parameter. The next switching state can be chosen by minimizing a cost function to control the inverter directly in the next sampling interval. For comparison purposes, a traditional hysteresis current control (HCC) and the proposed predictive current control are adopted to test their current tracking performance under different operations through some simulations.
Keywords
electric current control; hysteresis; invertors; machine control; predictive control; reluctance motor drives; stators; CSBPCC; HCC; SynRM; cost function; current sensor based predictive current control; current tracking performance; current variations; hysteresis current control; sampling interval; six-switch three-phase inverter; stator currents; synchronous reluctance motor drives; Current control; Inverters; Matrix converters; Permanent magnet motors; Predictive models; Stators; Switches; SynRM; predictive current control; three-phase inverter;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Green Building and Smart Grid (IGBSG), 2014 International Conference on
Conference_Location
Taipei
Type
conf
DOI
10.1109/IGBSG.2014.6835231
Filename
6835231
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