Data-driven power electronic converter modeling for low inertia power system dynamic studies

Nischal Guruwacharya; Niranjan Bhujel; Ujjwol Tamrakar; Manisha Rauniyar; Sunil Subedi; Sterling E. Berg; Timothy M. Hansen; Reinaldo Tonkoski

doi:10.1109/PESGM41954.2020.9281783

Data-driven power electronic converter modeling for low inertia power system dynamic studies

Nischal Guruwacharya, Niranjan Bhujel, Ujjwol Tamrakar, Manisha Rauniyar, Sunil Subedi, Sterling E. Berg, Timothy M. Hansen, Reinaldo Tonkoski

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

16 Scopus citations

Abstract

A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.

Original language	English
Title of host publication	2020 IEEE Power and Energy Society General Meeting, PESGM 2020
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728155081
DOIs	https://doi.org/10.1109/PESGM41954.2020.9281783
State	Published - Aug 2 2020
Externally published	Yes
Event	2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada Duration: Aug 2 2020 → Aug 6 2020

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2020-August
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2020 IEEE Power and Energy Society General Meeting, PESGM 2020
Country/Territory	Canada
City	Montreal
Period	08/2/20 → 08/6/20

Funding

This work is supported by the U.S. National Science Foundation under Grant Number MRI-1726964 and the U.S. Department of Energy under Grant Number DE-SC0020281.

Funders	Funder number
U.S. National Science Foundation
National Science Foundation	MRI-1726964
U.S. Department of Energy	DE-SC0020281
Directorate for Engineering	1726964

Keywords

Converter-dominated electric power systems
Data-driven modeling
Grid-connected converters
System identification

UN SDGs

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

Access to Document

10.1109/PESGM41954.2020.9281783

Cite this

Guruwacharya, N., Bhujel, N., Tamrakar, U., Rauniyar, M., Subedi, S., Berg, S. E., Hansen, T. M., & Tonkoski, R. (2020). Data-driven power electronic converter modeling for low inertia power system dynamic studies. In 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 Article 9281783 (IEEE Power and Energy Society General Meeting; Vol. 2020-August). IEEE Computer Society. https://doi.org/10.1109/PESGM41954.2020.9281783

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title = "Data-driven power electronic converter modeling for low inertia power system dynamic studies",

abstract = "A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.",

keywords = "Converter-dominated electric power systems, Data-driven modeling, Grid-connected converters, System identification",

author = "Nischal Guruwacharya and Niranjan Bhujel and Ujjwol Tamrakar and Manisha Rauniyar and Sunil Subedi and Berg, {Sterling E.} and Hansen, {Timothy M.} and Reinaldo Tonkoski",

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doi = "10.1109/PESGM41954.2020.9281783",

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Guruwacharya, N, Bhujel, N, Tamrakar, U, Rauniyar, M, Subedi, S, Berg, SE, Hansen, TM & Tonkoski, R 2020, Data-driven power electronic converter modeling for low inertia power system dynamic studies. in 2020 IEEE Power and Energy Society General Meeting, PESGM 2020., 9281783, IEEE Power and Energy Society General Meeting, vol. 2020-August, IEEE Computer Society, 2020 IEEE Power and Energy Society General Meeting, PESGM 2020, Montreal, Canada, 08/2/20. https://doi.org/10.1109/PESGM41954.2020.9281783

Data-driven power electronic converter modeling for low inertia power system dynamic studies. / Guruwacharya, Nischal; Bhujel, Niranjan; Tamrakar, Ujjwol et al.
2020 IEEE Power and Energy Society General Meeting, PESGM 2020. IEEE Computer Society, 2020. 9281783 (IEEE Power and Energy Society General Meeting; Vol. 2020-August).

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

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AU - Guruwacharya, Nischal

AU - Bhujel, Niranjan

AU - Tamrakar, Ujjwol

AU - Rauniyar, Manisha

AU - Subedi, Sunil

AU - Berg, Sterling E.

AU - Hansen, Timothy M.

AU - Tonkoski, Reinaldo

PY - 2020/8/2

Y1 - 2020/8/2

N2 - A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.

AB - A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.

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KW - Data-driven modeling

KW - Grid-connected converters

KW - System identification

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ER -

Data-driven power electronic converter modeling for low inertia power system dynamic studies

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

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