Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications

Mohamed Abdelrahem; Ibrahim Harbi; Mostafa Ahmed; M. Saad Bin Arif; Ralph Kennel; Jose Rodriguez

doi:10.1109/PRECEDE57319.2023.10174509

Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications

Mohamed Abdelrahem^*, Ibrahim Harbi, Mostafa Ahmed, M. Saad Bin Arif, Ralph Kennel, Jose Rodriguez

^*Corresponding author for this work

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

1 Scopus citations

Abstract

Finite-set model predictive control (FS-MPC) algorithms provide excellent dynamic performance for the power electronics converters with the ability to easily include any non-linearities and constraints. However, the high number of iterations required for prediction and cost function evaluation, especially for multi-level converters, hampers the implementation of the FS-MPC techniques. In this paper, firstly, the deadbeat concept is utilized to directly obtain the reference voltage vector. Accordingly, the iterations required for prediction are eliminated. Secondly, the number of iterations for cost function evaluation is also reduced by selecting a certain number of voltage vectors based on the value of the reference voltage vector. Finally, experimental results using hardware in the loop (HIL) technology are given to validate the proposed FS-MPC technique.

Original language	English
Title of host publication	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350396867
ISBN (Print)	9798350396867
DOIs	https://doi.org/10.1109/PRECEDE57319.2023.10174509
State	Published - 2023
Event	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 - Wuhan, China Duration: 2023 → 2023

Publication series

Name	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

Conference

Conference	2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Country/Territory	China
City	Wuhan
Period	16/06/23 → 19/06/23

Bibliographical note

Funding Information:
J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208 and 1221293

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

Control and Optimization
Modeling and Simulation
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Mechanical Engineering
Safety, Risk, Reliability and Quality

UN SDGs

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

Access to Document

10.1109/PRECEDE57319.2023.10174509

Cite this

Abdelrahem, M., Harbi, I., Ahmed, M., Arif, M. S. B., Kennel, R., & Rodriguez, J. (2023). Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications. In 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRECEDE57319.2023.10174509

Abdelrahem, Mohamed ; Harbi, Ibrahim ; Ahmed, Mostafa et al. / Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications. 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023).

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title = "Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications",

abstract = "Finite-set model predictive control (FS-MPC) algorithms provide excellent dynamic performance for the power electronics converters with the ability to easily include any non-linearities and constraints. However, the high number of iterations required for prediction and cost function evaluation, especially for multi-level converters, hampers the implementation of the FS-MPC techniques. In this paper, firstly, the deadbeat concept is utilized to directly obtain the reference voltage vector. Accordingly, the iterations required for prediction are eliminated. Secondly, the number of iterations for cost function evaluation is also reduced by selecting a certain number of voltage vectors based on the value of the reference voltage vector. Finally, experimental results using hardware in the loop (HIL) technology are given to validate the proposed FS-MPC technique.",

keywords = "cost function, deadbeat, model predictive control, multi-level converters",

author = "Mohamed Abdelrahem and Ibrahim Harbi and Mostafa Ahmed and Arif, {M. Saad Bin} and Ralph Kennel and Jose Rodriguez",

note = "Funding Information: J. Rodriguez acknowledges the support of ANID through projects FB0008, 1210208 and 1221293 Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 ; Conference date: 16-06-2023 Through 19-06-2023",

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doi = "10.1109/PRECEDE57319.2023.10174509",

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isbn = "9798350396867",

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Abdelrahem, M, Harbi, I, Ahmed, M, Arif, MSB, Kennel, R & Rodriguez, J 2023, Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications. in 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023, Wuhan, China, 16/06/23. https://doi.org/10.1109/PRECEDE57319.2023.10174509

Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications. / Abdelrahem, Mohamed; Harbi, Ibrahim; Ahmed, Mostafa et al.
2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023).

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

TY - GEN

T1 - Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications

AU - Abdelrahem, Mohamed

AU - Harbi, Ibrahim

AU - Ahmed, Mostafa

AU - Arif, M. Saad Bin

AU - Kennel, Ralph

AU - Rodriguez, Jose

PY - 2023

Y1 - 2023

N2 - Finite-set model predictive control (FS-MPC) algorithms provide excellent dynamic performance for the power electronics converters with the ability to easily include any non-linearities and constraints. However, the high number of iterations required for prediction and cost function evaluation, especially for multi-level converters, hampers the implementation of the FS-MPC techniques. In this paper, firstly, the deadbeat concept is utilized to directly obtain the reference voltage vector. Accordingly, the iterations required for prediction are eliminated. Secondly, the number of iterations for cost function evaluation is also reduced by selecting a certain number of voltage vectors based on the value of the reference voltage vector. Finally, experimental results using hardware in the loop (HIL) technology are given to validate the proposed FS-MPC technique.

AB - Finite-set model predictive control (FS-MPC) algorithms provide excellent dynamic performance for the power electronics converters with the ability to easily include any non-linearities and constraints. However, the high number of iterations required for prediction and cost function evaluation, especially for multi-level converters, hampers the implementation of the FS-MPC techniques. In this paper, firstly, the deadbeat concept is utilized to directly obtain the reference voltage vector. Accordingly, the iterations required for prediction are eliminated. Secondly, the number of iterations for cost function evaluation is also reduced by selecting a certain number of voltage vectors based on the value of the reference voltage vector. Finally, experimental results using hardware in the loop (HIL) technology are given to validate the proposed FS-MPC technique.

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Y2 - 16 June 2023 through 19 June 2023

ER -

Abdelrahem M, Harbi I, Ahmed M, Arif MSB, Kennel R, Rodriguez J. Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications. In 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023). doi: 10.1109/PRECEDE57319.2023.10174509

Finite-Set Model Predictive Control for 17-Level Inverter with Reduced Number of Iterations in Photovoltaic Applications

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

UN SDGs

Access to Document

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