DocumentCode
68935
Title
Performance of Multistep Finite Control Set Model Predictive Control for Power Electronics
Author
Geyer, Tobias ; Quevedo, D.E.
Author_Institution
ABB Corp. Res., Baden-Dättwil, Switzerland
Volume
30
Issue
3
fYear
2015
fDate
Mar-15
Firstpage
1633
Lastpage
1644
Abstract
The performance of direct model predictive control (MPC) with reference tracking and long prediction horizons is evaluated through simulations, using the current control problem of a variable speed drive system with a voltage source inverter as an illustrative example. A modified sphere decoding algorithm is used to efficiently solve the optimization problem underlying MPC for long horizons. For a horizon of five and a three-level inverter, for example, the computational burden is reduced by four orders of magnitude, compared to the standard exhaustive search approach. This paper illustrates the performance gains that are achievable by using prediction horizons larger than one. Specifically, for long prediction horizons and a low switching frequency, the total harmonic distortion of the current is significantly lower than for space vector modulation, making direct MPC with long horizons an attractive and computationally viable control scheme.
Keywords
electric current control; harmonic distortion; invertors; optimisation; predictive control; variable speed drives; MPC; current control problem; long prediction horizons; multistep finite control set model predictive control; power electronics; reference tracking; sphere decoding algorithm; three-level inverter; variable speed drive system; voltage source inverter; Inverters; Modulation; Optimization; Predictive control; Support vector machines; Switches; Switching frequency; Branch and bound; drive systems; finite control set; model predictive control (MPC); power electronics; quantization; sphere decoding;
fLanguage
English
Journal_Title
Power Electronics, IEEE Transactions on
Publisher
ieee
ISSN
0885-8993
Type
jour
DOI
10.1109/TPEL.2014.2316173
Filename
6784404
Link To Document