The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modu...

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Main Authors: Ming-Guang Zhang, Bo Li
Format: Article
Language:English
Published: Wiley 2019-04-01
Series:The Journal of Engineering
Subjects:
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8627
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spelling doaj-af9e79c4d60940e4b6eee8243c8fcc312021-04-02T13:28:29ZengWileyThe Journal of Engineering2051-33052019-04-0110.1049/joe.2018.8627JOE.2018.8627The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction controlMing-Guang Zhang0Bo Li1School of Electrical and Information Engineering, Lanzhou University of TechnologySchool of Electrical and Information Engineering, Lanzhou University of TechnologyThe modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy.https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8627dynamic responsePI controlpredictive controlvoltage controlpower transmission controlelectric current controlHVDC power convertorsHVDC power transmissionoptimal controlpower conversion harmonicsprediction modelfeedback correctionrolling optimisationoptimal voltage controlinternal loop current controllerouter loop controllerlow dynamic responsemodel prediction controlmodular multilevel converterlow harmonic designmodular designdynamic response speedmodel predictive controlsubmodule voltage-balanced control strategyMMC-HVDC system simulation modellarge capacity energy conversionPI parametersPSCAD-EMTDC software platform
collection DOAJ
language English
format Article
sources DOAJ
author Ming-Guang Zhang
Bo Li
spellingShingle Ming-Guang Zhang
Bo Li
The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control
The Journal of Engineering
dynamic response
PI control
predictive control
voltage control
power transmission control
electric current control
HVDC power convertors
HVDC power transmission
optimal control
power conversion harmonics
prediction model
feedback correction
rolling optimisation
optimal voltage control
internal loop current controller
outer loop controller
low dynamic response
model prediction control
modular multilevel converter
low harmonic design
modular design
dynamic response speed
model predictive control
submodule voltage-balanced control strategy
MMC-HVDC system simulation model
large capacity energy conversion
PI parameters
PSCAD-EMTDC software platform
author_facet Ming-Guang Zhang
Bo Li
author_sort Ming-Guang Zhang
title The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control
title_short The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control
title_full The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control
title_fullStr The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control
title_full_unstemmed The sub-module voltage-balanced control strategy of mmc-HVDC based on model prediction control
title_sort sub-module voltage-balanced control strategy of mmc-hvdc based on model prediction control
publisher Wiley
series The Journal of Engineering
issn 2051-3305
publishDate 2019-04-01
description The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy.
topic dynamic response
PI control
predictive control
voltage control
power transmission control
electric current control
HVDC power convertors
HVDC power transmission
optimal control
power conversion harmonics
prediction model
feedback correction
rolling optimisation
optimal voltage control
internal loop current controller
outer loop controller
low dynamic response
model prediction control
modular multilevel converter
low harmonic design
modular design
dynamic response speed
model predictive control
submodule voltage-balanced control strategy
MMC-HVDC system simulation model
large capacity energy conversion
PI parameters
PSCAD-EMTDC software platform
url https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8627
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