Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors

Muyu Chen; Zhige Yuan; Faramarz Faraji; Amer M.Y.M. Ghias; Honnyong Cha; Vun Chan Hua Nicholas

doi:10.1109/IESES53571.2023.10253745

Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors

Muyu Chen
, Zhige Yuan
, Faramarz Faraji
, Amer M.Y.M. Ghias
, Honnyong Cha
, Vun Chan Hua Nicholas

Nanyang Technological University
Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper presents the finite control set model predictive control (FCS-MPC) with improved control objectives using optimized weighting factors. The Pareto curve obtained from the Particle Swarm optimizes the weighting factors. The optimal weighting factors are then used in the FCS-MPC algorithm to enhance the control objective performance of a three-phase five-level current source converter (5L-CSC). In 5L-CSC, two identical three-level CSC (3L-CSC) modules are connected in parallel with one common current source supply to provide uniform five-level output current waveform. This CSC requires a current balance to operate the converter safely. Therefore, the control objective of the MPC is to control the output voltage and balance the internal currents. The weighting factors are optimized based on the converter's total harmonics distortion (THD) performance and inductor current ripple. The performance enhancement of 5L-CSC, utilizing optimal weighting factors, is confirmed through MATLAB simulations when compared to the model employing arbitrarily assigned weighting factors. The simulation outcomes additionally indicate that the integration of Particle Swarm Optimization (PSO) with FCS-MPC exhibits resilience across various condition settings.

Original language	English
Title of host publication	2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350324754
DOIs	https://doi.org/10.1109/IESES53571.2023.10253745
State	Published - 2023
Event	3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023 - Shanghai, China Duration: 26 Jul 2023 → 28 Jul 2023

Publication series

Name	2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

Conference

Conference	3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023
Country/Territory	China
City	Shanghai
Period	26/07/23 → 28/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Current Source Converter
Finite Control Set Model Predictive Control
Multi-Objective Particle Swarm Optimization
Weighting Factor Optimization

Access to Document

10.1109/IESES53571.2023.10253745

Cite this

Chen, M., Yuan, Z., Faraji, F., Ghias, A. M. Y. M., Cha, H., & Nicholas, V. C. H. (2023). Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors. In 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023 (2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IESES53571.2023.10253745

Chen, Muyu ; Yuan, Zhige ; Faraji, Faramarz et al. / Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors. 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023).

@inproceedings{067362107afc4251b5993c39f1985244,

title = "Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors",

abstract = "This paper presents the finite control set model predictive control (FCS-MPC) with improved control objectives using optimized weighting factors. The Pareto curve obtained from the Particle Swarm optimizes the weighting factors. The optimal weighting factors are then used in the FCS-MPC algorithm to enhance the control objective performance of a three-phase five-level current source converter (5L-CSC). In 5L-CSC, two identical three-level CSC (3L-CSC) modules are connected in parallel with one common current source supply to provide uniform five-level output current waveform. This CSC requires a current balance to operate the converter safely. Therefore, the control objective of the MPC is to control the output voltage and balance the internal currents. The weighting factors are optimized based on the converter's total harmonics distortion (THD) performance and inductor current ripple. The performance enhancement of 5L-CSC, utilizing optimal weighting factors, is confirmed through MATLAB simulations when compared to the model employing arbitrarily assigned weighting factors. The simulation outcomes additionally indicate that the integration of Particle Swarm Optimization (PSO) with FCS-MPC exhibits resilience across various condition settings.",

keywords = "Current Source Converter, Finite Control Set Model Predictive Control, Multi-Objective Particle Swarm Optimization, Weighting Factor Optimization",

author = "Muyu Chen and Zhige Yuan and Faramarz Faraji and Ghias, \{Amer M.Y.M.\} and Honnyong Cha and Nicholas, \{Vun Chan Hua\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023 ; Conference date: 26-07-2023 Through 28-07-2023",

year = "2023",

doi = "10.1109/IESES53571.2023.10253745",

language = "English",

series = "2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023",

address = "United States",

}

Chen, M, Yuan, Z, Faraji, F, Ghias, AMYM, Cha, H & Nicholas, VCH 2023, Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors. in 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023. 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023, Institute of Electrical and Electronics Engineers Inc., 3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023, Shanghai, China, 26/07/23. https://doi.org/10.1109/IESES53571.2023.10253745

Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors. / Chen, Muyu; Yuan, Zhige; Faraji, Faramarz et al.
2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors

AU - Chen, Muyu

AU - Yuan, Zhige

AU - Faraji, Faramarz

AU - Ghias, Amer M.Y.M.

AU - Cha, Honnyong

AU - Nicholas, Vun Chan Hua

PY - 2023

Y1 - 2023

N2 - This paper presents the finite control set model predictive control (FCS-MPC) with improved control objectives using optimized weighting factors. The Pareto curve obtained from the Particle Swarm optimizes the weighting factors. The optimal weighting factors are then used in the FCS-MPC algorithm to enhance the control objective performance of a three-phase five-level current source converter (5L-CSC). In 5L-CSC, two identical three-level CSC (3L-CSC) modules are connected in parallel with one common current source supply to provide uniform five-level output current waveform. This CSC requires a current balance to operate the converter safely. Therefore, the control objective of the MPC is to control the output voltage and balance the internal currents. The weighting factors are optimized based on the converter's total harmonics distortion (THD) performance and inductor current ripple. The performance enhancement of 5L-CSC, utilizing optimal weighting factors, is confirmed through MATLAB simulations when compared to the model employing arbitrarily assigned weighting factors. The simulation outcomes additionally indicate that the integration of Particle Swarm Optimization (PSO) with FCS-MPC exhibits resilience across various condition settings.

AB - This paper presents the finite control set model predictive control (FCS-MPC) with improved control objectives using optimized weighting factors. The Pareto curve obtained from the Particle Swarm optimizes the weighting factors. The optimal weighting factors are then used in the FCS-MPC algorithm to enhance the control objective performance of a three-phase five-level current source converter (5L-CSC). In 5L-CSC, two identical three-level CSC (3L-CSC) modules are connected in parallel with one common current source supply to provide uniform five-level output current waveform. This CSC requires a current balance to operate the converter safely. Therefore, the control objective of the MPC is to control the output voltage and balance the internal currents. The weighting factors are optimized based on the converter's total harmonics distortion (THD) performance and inductor current ripple. The performance enhancement of 5L-CSC, utilizing optimal weighting factors, is confirmed through MATLAB simulations when compared to the model employing arbitrarily assigned weighting factors. The simulation outcomes additionally indicate that the integration of Particle Swarm Optimization (PSO) with FCS-MPC exhibits resilience across various condition settings.

KW - Current Source Converter

KW - Finite Control Set Model Predictive Control

KW - Multi-Objective Particle Swarm Optimization

KW - Weighting Factor Optimization

UR - https://www.scopus.com/pages/publications/85174272198

U2 - 10.1109/IESES53571.2023.10253745

DO - 10.1109/IESES53571.2023.10253745

M3 - Conference contribution

AN - SCOPUS:85174272198

T3 - 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

BT - 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

Y2 - 26 July 2023 through 28 July 2023

ER -

Chen M, Yuan Z, Faraji F, Ghias AMYM, Cha H, Nicholas VCH. Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors. In 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023). doi: 10.1109/IESES53571.2023.10253745

Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors

Abstract

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UN SDGs

Keywords

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