NDLI: Model Predictive Direct Torque Control of a variable speed drive with a five-level inverter

Content Provider	IEEE Xplore Digital Library
Author	Geyer, T. Papafotiou, G.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, The University of Auckland, New Zealand (Geyer, T.) \|\| ABB Corporate Research, 5405 Baden-D¿attwil, Switzerland (Papafotiou, G.)
Abstract	Model Predictive Direct Torque Control (MPDTC) is a newly developed computational control method for mediumvoltage drives, which, based on the principles of Model Predictive Control, reduces the converter's switching losses and improves the torque's Total Harmonic Distortion (THD) with respect to standard Direct Torque Control (DTC), while maintaining DTC's favorable dynamic and robustness properties. In this paper, MPDTC is adapted and applied to a five-level converter driving a high frequency induction machine. This application poses the challenge to produce five-level waveforms within the (short) fundamental period while respecting the converter's thermal limitations. The MPDTC methodology is tailored to the specific application by decomposing the controller into a machine and three phase controllers. Initial results suggest that, with respect to Pulse Width Modulation (PWM), MPDTC is capable of simultaneously reducing the switching losses and the torque THD, both by roughly 50 %.
Starting Page	1203
Ending Page	1208
File Size	700893
Page Count	6
File Format	PDF
ISBN	9781424446483
ISSN	1553572X
e-ISBN	9781424446506
DOI	10.1109/IECON.2009.5414663
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-03
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Torque control Variable speed drives Pulse width modulation Switching loss Pulse width modulation inverters Medium voltage Predictive control Switching converters Torque converters
Content Type	Text
Resource Type	Article

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