Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios

Li Cheng; Xiongfei Wang; Huoming Yang; Lars Nordstrom

doi:10.1109/IECON49645.2022.9968458

Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios

Li Cheng
, Xiongfei Wang
, Huoming Yang
, Lars Nordstrom

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

1 Citation (Scopus)

Abstract

Current control of grid-connected converters may result in harmonic instability when grid impedance changes. To prevent this issue, current controller parameters can be tuned adaptively according to different short-circuit ratios (SCRs). It is thus important to estimate the grid impedance in real-time. Unlike traditional FFT-based impedance measurement methods, a more efficient estimation approach based on neural networks is proposed in this paper. This method does not require a fixed and relatively long sampling window, making it possible for real-time impedance measurement. Further, a step-by-step design method of the feedforward neural network (FNN) used for grid impedance estimation is developed. Time-domain simulation results validate the effectiveness of the approach. Based on the designed FNN, adaptive current control is implemented and verified through simulation.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	2022
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9968458
Publication status	Published - 2022
Externally published	Yes
Event	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Conference

Conference	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/2022 → 20/10/2022

Series	IECON Proceedings (Industrial Electronics Conference)
Volume	2022-October

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

adaptive current control
Feedforward neural network
grid impedance estimation

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10.1109/IECON49645.2022.9968458

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Cite this

@inproceedings{c0aa24cdfb9f4ba683488d40a626db56,

title = "Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios",

abstract = "Current control of grid-connected converters may result in harmonic instability when grid impedance changes. To prevent this issue, current controller parameters can be tuned adaptively according to different short-circuit ratios (SCRs). It is thus important to estimate the grid impedance in real-time. Unlike traditional FFT-based impedance measurement methods, a more efficient estimation approach based on neural networks is proposed in this paper. This method does not require a fixed and relatively long sampling window, making it possible for real-time impedance measurement. Further, a step-by-step design method of the feedforward neural network (FNN) used for grid impedance estimation is developed. Time-domain simulation results validate the effectiveness of the approach. Based on the designed FNN, adaptive current control is implemented and verified through simulation.",

keywords = "adaptive current control, Feedforward neural network, grid impedance estimation",

author = "Li Cheng and Xiongfei Wang and Huoming Yang and Lars Nordstrom",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9968458",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

booktitle = "IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

}

Cheng, L, Wang, X, Yang, H & Nordstrom, L 2022, Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers), IECON Proceedings (Industrial Electronics Conference), vol. 2022-October, 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, Belgium, 17/10/2022. https://doi.org/10.1109/IECON49645.2022.9968458

Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios. / Cheng, Li; Wang, Xiongfei; Yang, Huoming et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers), 2022. (IECON Proceedings (Industrial Electronics Conference), Vol. 2022-October).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios

AU - Cheng, Li

AU - Wang, Xiongfei

AU - Yang, Huoming

AU - Nordstrom, Lars

PY - 2022

Y1 - 2022

N2 - Current control of grid-connected converters may result in harmonic instability when grid impedance changes. To prevent this issue, current controller parameters can be tuned adaptively according to different short-circuit ratios (SCRs). It is thus important to estimate the grid impedance in real-time. Unlike traditional FFT-based impedance measurement methods, a more efficient estimation approach based on neural networks is proposed in this paper. This method does not require a fixed and relatively long sampling window, making it possible for real-time impedance measurement. Further, a step-by-step design method of the feedforward neural network (FNN) used for grid impedance estimation is developed. Time-domain simulation results validate the effectiveness of the approach. Based on the designed FNN, adaptive current control is implemented and verified through simulation.

AB - Current control of grid-connected converters may result in harmonic instability when grid impedance changes. To prevent this issue, current controller parameters can be tuned adaptively according to different short-circuit ratios (SCRs). It is thus important to estimate the grid impedance in real-time. Unlike traditional FFT-based impedance measurement methods, a more efficient estimation approach based on neural networks is proposed in this paper. This method does not require a fixed and relatively long sampling window, making it possible for real-time impedance measurement. Further, a step-by-step design method of the feedforward neural network (FNN) used for grid impedance estimation is developed. Time-domain simulation results validate the effectiveness of the approach. Based on the designed FNN, adaptive current control is implemented and verified through simulation.

KW - adaptive current control

KW - Feedforward neural network

KW - grid impedance estimation

UR - https://www.scopus.com/pages/publications/85143905777

U2 - 10.1109/IECON49645.2022.9968458

DO - 10.1109/IECON49645.2022.9968458

M3 - Article in proceeding

AN - SCOPUS:85143905777

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE (Institute of Electrical and Electronics Engineers)

T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Cheng L, Wang X, Yang H, Nordstrom L. Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). 2022. (IECON Proceedings (Industrial Electronics Conference), Vol. 2022-October). doi: 10.1109/IECON49645.2022.9968458

Design of Neural Network for Adaptive Current Control with Different Short-Circuit Ratios

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Keywords

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