Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters

Rashad Ghassani; Antoneta Iuliana Bratcu; Remus Teodorescu

doi:10.1016/j.ifacol.2022.07.075

Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters

Rashad Ghassani, Antoneta Iuliana Bratcu, Remus Teodorescu

Publikation: Bidrag til tidsskrift › Konferenceartikel i tidsskrift › Forskning › peer review

2 Citationer (Scopus)

20 Downloads (Pure)

Abstract

This paper discusses the application of a genetic algorithm (GA) to control system design for Modular Multilevel Converters (MMCs). In particular, genetic algorithm is used to compute the gains of a state-feedback controller for multi-input/multi-output (MIMO) plant model. This GA-optimized state-feedback controller is used to control both grid and circulating current of the MMC. This assures that the two currents' input-coupled dynamics are managed using a MIMO strategy. A detailed MATLAB®/Simulink® model of a three-phase MMC is further used to validate the proposed control technique. Different simulations show that the GA-optimized state-feedback controller outperforms the conventional cascaded control.

Originalsprog	Engelsk
Bogserie	IFAC-PapersOnLine
Vol/bind	55
Udgave nummer	9
Sider (fra-til)	431-436
Antal sider	6
ISSN	1474-6670
DOI	https://doi.org/10.1016/j.ifacol.2022.07.075
Status	Udgivet - 2022
Begivenhed	11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022 - Online, Serbien Varighed: 21 jun. 2022 → 23 jun. 2022

Konference

Konference	11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022
Land/Område	Serbien
By	Online
Periode	21/06/2022 → 23/06/2022

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© 2022 Elsevier B.V.. All rights reserved.

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10.1016/j.ifacol.2022.07.075Licens: CC BY-NC-ND 4.0

Open Access articleForlagets udgivne version, 1,58 MBLicens: CC BY-NC-ND 4.0

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Citationsformater

@inproceedings{eb17766511a74d31874b0dafd0b3218e,

title = "Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters",

abstract = "This paper discusses the application of a genetic algorithm (GA) to control system design for Modular Multilevel Converters (MMCs). In particular, genetic algorithm is used to compute the gains of a state-feedback controller for multi-input/multi-output (MIMO) plant model. This GA-optimized state-feedback controller is used to control both grid and circulating current of the MMC. This assures that the two currents' input-coupled dynamics are managed using a MIMO strategy. A detailed MATLAB{\textregistered}/Simulink{\textregistered} model of a three-phase MMC is further used to validate the proposed control technique. Different simulations show that the GA-optimized state-feedback controller outperforms the conventional cascaded control.",

keywords = "cascaded control, genetic algorithm, modular multilevel converter, state-feedback",

author = "Rashad Ghassani and Bratcu, {Antoneta Iuliana} and Remus Teodorescu",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.. All rights reserved.; 11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022 ; Conference date: 21-06-2022 Through 23-06-2022",

year = "2022",

doi = "10.1016/j.ifacol.2022.07.075",

language = "English",

volume = "55",

pages = "431--436",

journal = "IFAC-PapersOnLine",

issn = "1474-6670",

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TY - GEN

T1 - Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters

AU - Ghassani, Rashad

AU - Bratcu, Antoneta Iuliana

AU - Teodorescu, Remus

PY - 2022

Y1 - 2022

N2 - This paper discusses the application of a genetic algorithm (GA) to control system design for Modular Multilevel Converters (MMCs). In particular, genetic algorithm is used to compute the gains of a state-feedback controller for multi-input/multi-output (MIMO) plant model. This GA-optimized state-feedback controller is used to control both grid and circulating current of the MMC. This assures that the two currents' input-coupled dynamics are managed using a MIMO strategy. A detailed MATLAB®/Simulink® model of a three-phase MMC is further used to validate the proposed control technique. Different simulations show that the GA-optimized state-feedback controller outperforms the conventional cascaded control.

AB - This paper discusses the application of a genetic algorithm (GA) to control system design for Modular Multilevel Converters (MMCs). In particular, genetic algorithm is used to compute the gains of a state-feedback controller for multi-input/multi-output (MIMO) plant model. This GA-optimized state-feedback controller is used to control both grid and circulating current of the MMC. This assures that the two currents' input-coupled dynamics are managed using a MIMO strategy. A detailed MATLAB®/Simulink® model of a three-phase MMC is further used to validate the proposed control technique. Different simulations show that the GA-optimized state-feedback controller outperforms the conventional cascaded control.

KW - cascaded control

KW - genetic algorithm

KW - modular multilevel converter

KW - state-feedback

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U2 - 10.1016/j.ifacol.2022.07.075

DO - 10.1016/j.ifacol.2022.07.075

M3 - Conference article in Journal

AN - SCOPUS:85137154801

SN - 1474-6670

VL - 55

SP - 431

EP - 436

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 9

T2 - 11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022

Y2 - 21 June 2022 through 23 June 2022

ER -

Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters

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