Sequential Model Predictive Control of Stand-Alone Voltage Source Inverters

Changming Zheng, Tomislav Dragicevic, Minrui Leng, Jose Rodriguez, Frede Blaabjerg

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

To avoid the time-consuming weighting-factor tuning work in conventional finite control set model predictive control (FCS-MPC), a sequential model predictive control (SMPC) scheme for stand-alone voltage source inverters of an islanded ac microgrid is proposed in this paper. The main idea is that two cost functions for separately minimizing the capacitor voltage and the inductor current tracking errors of the $LC$ filter are deployed in a sequential structure instead of being integrated into a single cost function. As a result, the weighting factor for balancing the two control objectives is eliminated. Moreover, the proposed SMPC does not degrade the output-voltage control performance too much. Simulation results are provided, verifying the effectiveness of the presented approach.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems
PublisherIEEE
Publication date2020
Pages409-412
ISBN (Print)9781728169897
DOIs
Publication statusPublished - 2020
EventIEEE 11th International Symposium on Power Electronics for Distributed Generation Systems - Virtual event, Dubrovnik, Croatia
Duration: 28 Sep 20201 Oct 2020

Conference

ConferenceIEEE 11th International Symposium on Power Electronics for Distributed Generation Systems
LocationVirtual event
CountryCroatia
CityDubrovnik
Period28/09/202001/10/2020
Series2020 Ieee 11th International Symposium on Power Electronics for Distributed Generation Systems (pedg)
ISSN2329-5767

Keywords

  • Model predictive control,
  • LC filter
  • Inverters
  • Microgrid
  • Weighting factor design

Fingerprint Dive into the research topics of 'Sequential Model Predictive Control of Stand-Alone Voltage Source Inverters'. Together they form a unique fingerprint.

Cite this