Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters

Mengfan Zhang; Xiongfei Wang; Dongsheng Yang; Zihao Cui; Mads Grasboll Christensen

doi:10.1109/ECCE44975.2020.9236090

Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters

Mengfan Zhang, Xiongfei Wang, Dongsheng Yang, Zihao Cui, Mads Grasboll Christensen

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

14 Citations (Scopus)

Abstract

The black-box impedance model of the voltage source inverters (VSIs) can be directly identified at the converter terminal without access to its internal control details, which greatly facilitate the converter-grid interactions. However, since the converter is inherently a nonlinear system, the measured converter impedance model will change along with the operating point. As the limited data amount in practical industrial applications, the existing impedance identification method cannot capture this operating point-dependent feature of the impedance model. In this paper, the model-based transfer learning method is employed to generate the operating-point dependent impedance model. This method can significantly reduce the required data amount used in model training so that the machine learning-based method could be applied for the practical industrial application. The comparison results confirm the accuracy of the impedance model obtained by this data-driven impedance identification method.

Original language	English
Title of host publication	ECCE 2020 - IEEE Energy Conversion Congress and Exposition
Number of pages	5
Publisher	IEEE
Publication date	Oct 2020
Pages	6170-6174
Article number	9236090
ISBN (Electronic)	9781728158266
DOIs	https://doi.org/10.1109/ECCE44975.2020.9236090
Publication status	Published - Oct 2020
Event	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States Duration: 11 Oct 2020 → 15 Oct 2020

Conference

Conference	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Country/Territory	United States
City	Virtual, Detroit
Period	11/10/2020 → 15/10/2020
Sponsor	IEEE Industrial Application Society (IAS), IEEE Power Electronics Society (PELS)

Series	ECCE 2020 - IEEE Energy Conversion Congress and Exposition

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Deep neural network
impedance identification
operating point variation
transfer learning
voltage source inverter

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Cite this

@inproceedings{8ce7f77897a649559010f2cddbadae69,

title = "Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters",

abstract = "The black-box impedance model of the voltage source inverters (VSIs) can be directly identified at the converter terminal without access to its internal control details, which greatly facilitate the converter-grid interactions. However, since the converter is inherently a nonlinear system, the measured converter impedance model will change along with the operating point. As the limited data amount in practical industrial applications, the existing impedance identification method cannot capture this operating point-dependent feature of the impedance model. In this paper, the model-based transfer learning method is employed to generate the operating-point dependent impedance model. This method can significantly reduce the required data amount used in model training so that the machine learning-based method could be applied for the practical industrial application. The comparison results confirm the accuracy of the impedance model obtained by this data-driven impedance identification method.",

keywords = "Deep neural network, impedance identification, operating point variation, transfer learning, voltage source inverter",

author = "Mengfan Zhang and Xiongfei Wang and Dongsheng Yang and Zihao Cui and Christensen, {Mads Grasboll}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 ; Conference date: 11-10-2020 Through 15-10-2020",

year = "2020",

month = oct,

doi = "10.1109/ECCE44975.2020.9236090",

language = "English",

series = "ECCE 2020 - IEEE Energy Conversion Congress and Exposition",

pages = "6170--6174",

booktitle = "ECCE 2020 - IEEE Energy Conversion Congress and Exposition",

publisher = "IEEE",

address = "United States",

}

Zhang, M, Wang, X, Yang, D, Cui, Z & Christensen, MG 2020, Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters. in ECCE 2020 - IEEE Energy Conversion Congress and Exposition., 9236090, IEEE, ECCE 2020 - IEEE Energy Conversion Congress and Exposition, pp. 6170-6174, 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020, Virtual, Detroit, United States, 11/10/2020. https://doi.org/10.1109/ECCE44975.2020.9236090

Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters. / Zhang, Mengfan; Wang, Xiongfei; Yang, Dongsheng et al.
ECCE 2020 - IEEE Energy Conversion Congress and Exposition. IEEE, 2020. p. 6170-6174 9236090 (ECCE 2020 - IEEE Energy Conversion Congress and Exposition).

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

TY - GEN

T1 - Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters

AU - Zhang, Mengfan

AU - Wang, Xiongfei

AU - Yang, Dongsheng

AU - Cui, Zihao

AU - Christensen, Mads Grasboll

PY - 2020/10

Y1 - 2020/10

N2 - The black-box impedance model of the voltage source inverters (VSIs) can be directly identified at the converter terminal without access to its internal control details, which greatly facilitate the converter-grid interactions. However, since the converter is inherently a nonlinear system, the measured converter impedance model will change along with the operating point. As the limited data amount in practical industrial applications, the existing impedance identification method cannot capture this operating point-dependent feature of the impedance model. In this paper, the model-based transfer learning method is employed to generate the operating-point dependent impedance model. This method can significantly reduce the required data amount used in model training so that the machine learning-based method could be applied for the practical industrial application. The comparison results confirm the accuracy of the impedance model obtained by this data-driven impedance identification method.

AB - The black-box impedance model of the voltage source inverters (VSIs) can be directly identified at the converter terminal without access to its internal control details, which greatly facilitate the converter-grid interactions. However, since the converter is inherently a nonlinear system, the measured converter impedance model will change along with the operating point. As the limited data amount in practical industrial applications, the existing impedance identification method cannot capture this operating point-dependent feature of the impedance model. In this paper, the model-based transfer learning method is employed to generate the operating-point dependent impedance model. This method can significantly reduce the required data amount used in model training so that the machine learning-based method could be applied for the practical industrial application. The comparison results confirm the accuracy of the impedance model obtained by this data-driven impedance identification method.

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KW - transfer learning

KW - voltage source inverter

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BT - ECCE 2020 - IEEE Energy Conversion Congress and Exposition

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ER -

Transfer Learning for Identifying Impedance Estimation in Voltage Source Inverters

Abstract

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Keywords

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