Computation Reduction for Finite Set Model Predictive Control of Five-level Inverter

Alireza Ja'afari, S. Alireza Davari, Cristian Garcia, Jose Rodriguez

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

Abstract

Finite set model predictive control (FS-MPC) method is a method that requires the calculation of the cost function prediction due to the number of switching vectors on the power converter. As the number of vectors increases, so does the amount of computation. Nowadays, computational burden needs to be reduced due to the development of using FS-MPC in multilevel converters such as neutral point clamped (NPC) converter. This paper presents a method for reducing the computational burden for the fine-level NPC inverters in which only two candidates are included in the cost function among the available candidates. The rectangular area method is used in order to select the proper candidates for the cost function. The proposed method is evaluated by simulations.

Original languageEnglish
Title of host publication2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728158495
DOIs
Publication statusPublished - Feb 2020
Event11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020 - Tehran, Iran, Islamic Republic of
Duration: 4 Feb 20206 Feb 2020

Publication series

Name2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020

Conference

Conference11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
Country/TerritoryIran, Islamic Republic of
CityTehran
Period4/02/206/02/20

Keywords

  • Computational burden
  • Model Predictive Control (MPC)
  • multilevel inverter
  • Neutral Point Clamped (NPC)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization

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