Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine

Qing Chen; Xiaonan Gao; Peter Stolze; Ralph Kennel

doi:10.1109/ECCE47101.2021.9595879

Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine

Qing Chen
, Xiaonan Gao
, Peter Stolze
, Ralph Kennel

Chair of High-Power Converter Systems

Technical University of Munich

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

Abstract

This paper presents a finite control set model-based predictive current control (PCC) method with variable sampling interval for induction machine. The advantage of conventional PCC is that the optimized switching state takes place only at the beginning of a fixed sampling interval. However, its drawback is high ripples on controlled variables. Compared with traditional PCC, in the proposed PCC method the change of one optimized switching state takes place at the beginning of a variable sampling interval which can achieve a comparative performance on controlled variables. Simulation results are provided to verify the effectiveness of the proposed PCC method.

Original language	English
Title of host publication	2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5014-5021
Number of pages	8
ISBN (Electronic)	9781728151359
DOIs	https://doi.org/10.1109/ECCE47101.2021.9595879
State	Published - 2021
Event	13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canada Duration: 10 Oct 2021 → 14 Oct 2021

Publication series

Name	2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

Conference

Conference	13th IEEE Energy Conversion Congress and Exposition, ECCE 2021
Country/Territory	Canada
City	Virtual, Online
Period	10/10/21 → 14/10/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/ECCE47101.2021.9595879

Cite this

Chen, Q., Gao, X., Stolze, P., & Kennel, R. (2021). Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine. In 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings (pp. 5014-5021). (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE47101.2021.9595879

Chen, Qing ; Gao, Xiaonan ; Stolze, Peter et al. / Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine. 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 5014-5021 (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings).

@inproceedings{38e47120436e46a3acb6b0132240a476,

title = "Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine",

abstract = "This paper presents a finite control set model-based predictive current control (PCC) method with variable sampling interval for induction machine. The advantage of conventional PCC is that the optimized switching state takes place only at the beginning of a fixed sampling interval. However, its drawback is high ripples on controlled variables. Compared with traditional PCC, in the proposed PCC method the change of one optimized switching state takes place at the beginning of a variable sampling interval which can achieve a comparative performance on controlled variables. Simulation results are provided to verify the effectiveness of the proposed PCC method.",

author = "Qing Chen and Xiaonan Gao and Peter Stolze and Ralph Kennel",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 ; Conference date: 10-10-2021 Through 14-10-2021",

year = "2021",

doi = "10.1109/ECCE47101.2021.9595879",

language = "English",

series = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",

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

pages = "5014--5021",

booktitle = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",

}

Chen, Q, Gao, X, Stolze, P & Kennel, R 2021, Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine. in 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 5014-5021, 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021, Virtual, Online, Canada, 10/10/21. https://doi.org/10.1109/ECCE47101.2021.9595879

Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine. / Chen, Qing; Gao, Xiaonan; Stolze, Peter et al.
2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 5014-5021 (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings).

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

TY - GEN

T1 - Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine

AU - Chen, Qing

AU - Gao, Xiaonan

AU - Stolze, Peter

AU - Kennel, Ralph

PY - 2021

Y1 - 2021

N2 - This paper presents a finite control set model-based predictive current control (PCC) method with variable sampling interval for induction machine. The advantage of conventional PCC is that the optimized switching state takes place only at the beginning of a fixed sampling interval. However, its drawback is high ripples on controlled variables. Compared with traditional PCC, in the proposed PCC method the change of one optimized switching state takes place at the beginning of a variable sampling interval which can achieve a comparative performance on controlled variables. Simulation results are provided to verify the effectiveness of the proposed PCC method.

AB - This paper presents a finite control set model-based predictive current control (PCC) method with variable sampling interval for induction machine. The advantage of conventional PCC is that the optimized switching state takes place only at the beginning of a fixed sampling interval. However, its drawback is high ripples on controlled variables. Compared with traditional PCC, in the proposed PCC method the change of one optimized switching state takes place at the beginning of a variable sampling interval which can achieve a comparative performance on controlled variables. Simulation results are provided to verify the effectiveness of the proposed PCC method.

UR - https://www.scopus.com/pages/publications/85123382929

U2 - 10.1109/ECCE47101.2021.9595879

DO - 10.1109/ECCE47101.2021.9595879

M3 - Conference contribution

AN - SCOPUS:85123382929

T3 - 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

SP - 5014

EP - 5021

BT - 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021

Y2 - 10 October 2021 through 14 October 2021

ER -

Chen Q, Gao X, Stolze P, Kennel R. Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine. In 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 5014-5021. (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings). doi: 10.1109/ECCE47101.2021.9595879

Finite Control Set Model-Based Predictive Current Control with Variable Sampling Interval for Induction Machine

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this