Modularized Small-Signal Modeling of Grid-Forming Inverters

A wide variety of control schemes for Grid-Forming Inverters (GFMIs) have been developed to enable inverter-based resources (IBRs) to comply with evolving grid-code requirements and deliver essential support services such as frequency and voltage regulation. However, the diversity in these control structures presents substantial challenges for consistent modeling, analysis, and systematic comparison. Conventional approaches to modeling Grid-Forming Inverters typically focus on individual control strategies or isolated configurations, limiting their flexibility and scalability for broader applications. To overcome these limitations, this paper introduces a unified, modular framework utilizing state-space representation (SSR) and the Component Connection Method (CCM) for small-signal modeling of GFMIs. The proposed framework systematically accommodates four prevalent Active Power Control (APC) strategies—Droop control, Droop with Low-Pass Filter (LPF), Virtual Synchronous Generator (VSG), and Compensated Generalized Virtual Synchronous Generator (CGVSG). By establishing a comprehensive and modular modeling methodology, this paper facilitates efficient stability analysis, parameter sensitivity assessment, and performance optimization of diverse GFMI configurations under varying grid conditions. The effectiveness and accuracy of the proposed approach are demonstrated through detailed eigenvalue analyses and validated via time-domain simulations, illustrating significant implications for practical engineering design, grid code compliance, and operational stability in inverter-dominated power systems.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3572689
Citations
1
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.53

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article

Modularized Small-Signal Modeling of Grid-Forming Inverters

Paul Serna-Torre, Behrooz Bahrani, Mehdi Ghazavi Dozein, Patricia Hidalgo-Gonzalez et al.
1 citations
IEEE Access
Power Systems and Renewable Energy
0.53
article

Modularized Small-Signal Modeling of Grid-Forming Inverters

Paul Serna-Torre, Behrooz Bahrani, Mehdi Ghazavi Dozein, Patricia Hidalgo-Gonzalez, Sohail Ahmad Ali
article en
1 citations

Abstract

A wide variety of control schemes for Grid-Forming Inverters (GFMIs) have been developed to enable inverter-based resources (IBRs) to comply with evolving grid-code requirements and deliver essential support services such as frequency and voltage regulation. However, the diversity in these control structures presents substantial challenges for consistent modeling, analysis, and systematic comparison. Conventional approaches to modeling Grid-Forming Inverters typically focus on individual control strategies or isolated configurations, limiting their flexibility and scalability for broader applications. To overcome these limitations, this paper introduces a unified, modular framework utilizing state-space representation (SSR) and the Component Connection Method (CCM) for small-signal modeling of GFMIs. The proposed framework systematically accommodates four prevalent Active Power Control (APC) strategies—Droop control, Droop with Low-Pass Filter (LPF), Virtual Synchronous Generator (VSG), and Compensated Generalized Virtual Synchronous Generator (CGVSG). By establishing a comprehensive and modular modeling methodology, this paper facilitates efficient stability analysis, parameter sensitivity assessment, and performance optimization of diverse GFMI configurations under varying grid conditions. The effectiveness and accuracy of the proposed approach are demonstrated through detailed eigenvalue analyses and validated via time-domain simulations, illustrating significant implications for practical engineering design, grid code compliance, and operational stability in inverter-dominated power systems.

IEEE AccessVol. 13
University of California San Diego (US), Monash University (AU)
National Science Foundation
Openalex Percentile: Top 31%
Power Systems and Renewable Energy
0.53
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