Constrained Model Predictive Control in Nine-Phase Induction Motor Drives

The advent of powerful digital signal processors has recently permitted the real-time implementation of model predictive control (MPC) in high-performance electric drives. Nevertheless, the use of MPC together with multiphase systems is increasingly challenging as the number of phases gets higher. O...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on energy conversion Vol. 34; no. 4; pp. 1881 - 1889
Main Authors: Gonzalez-Prieto, Ignacio, Zoric, Ivan, Duran, Mario J., Levi, Emil
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computational efficiency
Constraints
Digital signal processors
Electric drives
Induction motor drives
Induction motors
model predictive control
Multiphase drives
Predictive control
Real-time systems
Redundancy
Signal processing
Switches
Switching
Switching frequency
ISSN:0885-8969, 1558-0059
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The advent of powerful digital signal processors has recently permitted the real-time implementation of model predictive control (MPC) in high-performance electric drives. Nevertheless, the use of MPC together with multiphase systems is increasingly challenging as the number of phases gets higher. On the positive side, the redundancy provided by the extra phases also opens the possibility to further optimize the control action. This paper describes the implementation of MPC for nine-phase drives using a three-step approach with an initial discarding of the switching states, a dynamic selection of the voltage vectors using hard constraints, and an improved performance including soft constraints. Experimental results confirm the ability of the proposed MPC to highly reduce the computational burden and switching frequency, while maintaining satisfactory steady state and dynamic performance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-8969
1558-0059
DOI:10.1109/TEC.2019.2929622