Predictive zero-sequence control of parallel three-phase active rectifiers

Luca Tarisciotti, Claudio Burgos, Cristian Garcia, Jose Rodriguez

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

Abstract

Model Predictive Control has been recently considered for many power electronics control applications. The reasons of such interest are the many benefits it can provide over classical control techniques. As for example of these advantages, Model Predictive Control avoid cascaded control loops, easily include non-linear constraints in the control algorithm and provides a fast dynamic. In particular, Finite Control Set Model Predictive Control directly considers the discrete power electronics converters state to perform the control action, thus simplifying the control derivation and implementation. This paper proposes an application of Finite Control Set Model Predictive Control to parallel three-phase active rectifiers control, where active and reactive power, DC voltage and zero sequence power are controlled within one single control loop. The proposed control approach is validated through simulation in several operating conditions.

Original languageEnglish
Title of host publicationECCE 2020 - IEEE Energy Conversion Congress and Exposition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2584-2591
Number of pages8
ISBN (Electronic)9781728158266
DOIs
Publication statusPublished - 11 Oct 2020
Event12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States
Duration: 11 Oct 202015 Oct 2020

Publication series

NameECCE 2020 - IEEE Energy Conversion Congress and Exposition

Conference

Conference12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Country/TerritoryUnited States
CityVirtual, Detroit
Period11/10/2015/10/20

Keywords

  • Power Electronics
  • Predictive Control
  • Three Phase Active Rectifier

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology

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