Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids

Apoorva Nandakumar; Yan Li; Dongbo Zhao; Yichen Zhang; Tianqi Hong

doi:10.1109/PESGM48719.2022.9917105

Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids

Apoorva Nandakumar
, Yan Li
, Dongbo Zhao
, Yichen Zhang
, Tianqi Hong

Electrical Engineering

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

10 Scopus citations

Abstract

Considering the widespread integration of power-electronic-interfaced distributed energy resources (DERs) in microgrids, the system's inertia is highly reduced. Thus, microgrids are sensitive to disturbances. To get the situational awareness of transient dynamics of microgrids when they face severe disturbances, a data-driven modeling algorithm, Sparse Identification of Non-Linear Dynamics (SINDy), is discussed in this paper. The governing equation of microgrids can be identified through SINDy, which determines the nonlinear dynamics of the system. Numerical examples test and verify the data-driven model based on SINDy can successfully capture the transient dynamics of microgrids. Simulation results have validated the data-driven model is reliable and very close to the actual test system. Therefore, it provides a potent tool to analyze the system's transient dynamics when an accurate system's model is unavailable.

Original language	English (US)
Title of host publication	2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665408233
DOIs	https://doi.org/10.1109/PESGM48719.2022.9917105
State	Published - 2022
Event	2022 IEEE Power and Energy Society General Meeting, PESGM 2022 - Denver, United States Duration: Jul 17 2022 → Jul 21 2022

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2022-July
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Country/Territory	United States
City	Denver
Period	7/17/22 → 7/21/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

Access to Document

10.1109/PESGM48719.2022.9917105

Cite this

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title = "Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids",

abstract = "Considering the widespread integration of power-electronic-interfaced distributed energy resources (DERs) in microgrids, the system's inertia is highly reduced. Thus, microgrids are sensitive to disturbances. To get the situational awareness of transient dynamics of microgrids when they face severe disturbances, a data-driven modeling algorithm, Sparse Identification of Non-Linear Dynamics (SINDy), is discussed in this paper. The governing equation of microgrids can be identified through SINDy, which determines the nonlinear dynamics of the system. Numerical examples test and verify the data-driven model based on SINDy can successfully capture the transient dynamics of microgrids. Simulation results have validated the data-driven model is reliable and very close to the actual test system. Therefore, it provides a potent tool to analyze the system's transient dynamics when an accurate system's model is unavailable.",

author = "Apoorva Nandakumar and Yan Li and Dongbo Zhao and Yichen Zhang and Tianqi Hong",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Power and Energy Society General Meeting, PESGM 2022 ; Conference date: 17-07-2022 Through 21-07-2022",

year = "2022",

doi = "10.1109/PESGM48719.2022.9917105",

language = "English (US)",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2022 IEEE Power and Energy Society General Meeting, PESGM 2022",

address = "United States",

}

Nandakumar, A, Li, Y, Zhao, D, Zhang, Y & Hong, T 2022, Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids. in 2022 IEEE Power and Energy Society General Meeting, PESGM 2022. IEEE Power and Energy Society General Meeting, vol. 2022-July, IEEE Computer Society, 2022 IEEE Power and Energy Society General Meeting, PESGM 2022, Denver, United States, 7/17/22. https://doi.org/10.1109/PESGM48719.2022.9917105

Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids. / Nandakumar, Apoorva; Li, Yan; Zhao, Dongbo et al.
2022 IEEE Power and Energy Society General Meeting, PESGM 2022. IEEE Computer Society, 2022. (IEEE Power and Energy Society General Meeting; Vol. 2022-July).

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

TY - GEN

T1 - Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids

AU - Nandakumar, Apoorva

AU - Li, Yan

AU - Zhao, Dongbo

AU - Zhang, Yichen

AU - Hong, Tianqi

PY - 2022

Y1 - 2022

N2 - Considering the widespread integration of power-electronic-interfaced distributed energy resources (DERs) in microgrids, the system's inertia is highly reduced. Thus, microgrids are sensitive to disturbances. To get the situational awareness of transient dynamics of microgrids when they face severe disturbances, a data-driven modeling algorithm, Sparse Identification of Non-Linear Dynamics (SINDy), is discussed in this paper. The governing equation of microgrids can be identified through SINDy, which determines the nonlinear dynamics of the system. Numerical examples test and verify the data-driven model based on SINDy can successfully capture the transient dynamics of microgrids. Simulation results have validated the data-driven model is reliable and very close to the actual test system. Therefore, it provides a potent tool to analyze the system's transient dynamics when an accurate system's model is unavailable.

AB - Considering the widespread integration of power-electronic-interfaced distributed energy resources (DERs) in microgrids, the system's inertia is highly reduced. Thus, microgrids are sensitive to disturbances. To get the situational awareness of transient dynamics of microgrids when they face severe disturbances, a data-driven modeling algorithm, Sparse Identification of Non-Linear Dynamics (SINDy), is discussed in this paper. The governing equation of microgrids can be identified through SINDy, which determines the nonlinear dynamics of the system. Numerical examples test and verify the data-driven model based on SINDy can successfully capture the transient dynamics of microgrids. Simulation results have validated the data-driven model is reliable and very close to the actual test system. Therefore, it provides a potent tool to analyze the system's transient dynamics when an accurate system's model is unavailable.

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U2 - 10.1109/PESGM48719.2022.9917105

DO - 10.1109/PESGM48719.2022.9917105

M3 - Conference contribution

AN - SCOPUS:85141447701

T3 - IEEE Power and Energy Society General Meeting

BT - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022

PB - IEEE Computer Society

T2 - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022

Y2 - 17 July 2022 through 21 July 2022

ER -

Sparse Identification-Enabled Data-Driven Modeling for Nonlinear Dynamics of Microgrids

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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