A Gray-Box Stability Analysis Mechanism for Power Electronic Converters

Abstract

This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc) algorithm. While retaining the advantages of eliminating the need for intrinsic controller information, the proposed gray-box method establishes higher accuracy and interpretable outcomes over the conventional DMD method. Finally under experimental conditions of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter, the proposed gray-box DMDc is verified to identify the dominant eigenvalues more accurately.

Original language	English
Title of host publication	2024 IEEE Applied Power Electronics Conference and Exposition (APEC)
Number of pages	6
Place of Publication	Long Beach, CA, USA
Publication date	2 May 2024
Pages	1310-1315
ISBN (Print)	979-8-3503-1665-0
ISBN (Electronic)	979-8-3503-1664-3
DOIs	https://doi.org/10.1109/APEC48139.2024.10509115
Publication status	Published - 2 May 2024

Keywords

Dynamic mode decomposition (DMD)
Gray-box method
Grid-tied converter
Mode Identification
Stability Analysis

Access to Document

10.1109/APEC48139.2024.10509115

1211

https://ieeexplore.ieee.org/document/10509115

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@inproceedings{c2950a531f2d4a22a82e62bcba4474e5,

title = "A Gray-Box Stability Analysis Mechanism for Power Electronic Converters",

abstract = "This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc) algorithm. While retaining the advantages of eliminating the need for intrinsic controller information, the proposed gray-box method establishes higher accuracy and interpretable outcomes over the conventional DMD method. Finally under experimental conditions of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter, the proposed gray-box DMDc is verified to identify the dominant eigenvalues more accurately.",

keywords = "Dynamic mode decomposition (DMD), Gray-box method, Grid-tied converter, Mode Identification, Stability Analysis",

author = "Rui Kong and Subham Sahoo and Yubo Song and Frede Blaabjerg",

year = "2024",

month = may,

day = "2",

doi = "10.1109/APEC48139.2024.10509115",

language = "English",

isbn = "979-8-3503-1665-0",

pages = "1310--1315",

booktitle = "2024 IEEE Applied Power Electronics Conference and Exposition (APEC)",

}

TY - GEN

T1 - A Gray-Box Stability Analysis Mechanism for Power Electronic Converters

AU - Kong, Rui

AU - Sahoo, Subham

AU - Song, Yubo

AU - Blaabjerg, Frede

PY - 2024/5/2

Y1 - 2024/5/2

N2 - This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc) algorithm. While retaining the advantages of eliminating the need for intrinsic controller information, the proposed gray-box method establishes higher accuracy and interpretable outcomes over the conventional DMD method. Finally under experimental conditions of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter, the proposed gray-box DMDc is verified to identify the dominant eigenvalues more accurately.

AB - This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc) algorithm. While retaining the advantages of eliminating the need for intrinsic controller information, the proposed gray-box method establishes higher accuracy and interpretable outcomes over the conventional DMD method. Finally under experimental conditions of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter, the proposed gray-box DMDc is verified to identify the dominant eigenvalues more accurately.

KW - Dynamic mode decomposition (DMD)

KW - Gray-box method

KW - Grid-tied converter

KW - Mode Identification

KW - Stability Analysis

UR - http://www.scopus.com/inward/record.url?scp=85192722577&partnerID=8YFLogxK

U2 - 10.1109/APEC48139.2024.10509115

DO - 10.1109/APEC48139.2024.10509115

M3 - Article in proceeding

SN - 979-8-3503-1665-0

SP - 1310

EP - 1315

BT - 2024 IEEE Applied Power Electronics Conference and Exposition (APEC)

CY - Long Beach, CA, USA

ER -