Indirect predictive control strategy with fixed switching frequency for a direct matrix converter

M. Rivera, L. Tarisciotti, P. Wheeler, S. Bayhan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper describes the application of a simplified indirect model predictive current control strategy for a direct Matrix Converter. The direct matrix converter has a large number of available switching states which implies that the implementation of predictive control high computational cost. In this paper, a predictive current control strategy is proposed in order to simplify the computational cost while avoiding the use of weighting factors. The method is based on the fictitious dc-link concept, which has been used in the past for the classical modulation and control techniques of the direct matrix converter. The proposed controller is enhanced with a fixed switching predictive strategy in order to improve the performance of the full system. Simulated results confirm the feasibility of the proposed controller demonstrating that it is an alternative to classical predictive control strategies for the direct matrix converter.

Original languageEnglish
Title of host publicationProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7332-7337
Number of pages6
ISBN (Electronic)9781538611272
DOIs
Publication statusPublished - 15 Dec 2017
Event43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017 - Beijing, China
Duration: 29 Oct 20171 Nov 2017

Publication series

NameProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Volume2017-January

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Country/TerritoryChina
CityBeijing
Period29/10/171/11/17

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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