Voltage Support Capability Analysis for Grid-Forming Inverters With Adaptive Virtual Impedance Under Asymmetrical Grid Faults

Current grid codes require grid-forming (GFM) inverters to provide voltage support capability during grid faults to ensure stability and resilience of power systems. While research on voltage support capability for GFM inverters with adaptive virtual impedance (AVI) under symmetrical grid faults has been conducted, its findings cannot be directly applied to asymmetrical grid faults (ASGFs). This limitation arises because ASGFs introduce negative- and zero-sequence networks, significantly complicating the theoretical analysis. Given the prevalence and complexity of ASGFs, a comprehensive theoretical framework for analyzing voltage support capability is essential. To address this gap, this article first presents the implementation of AVI for GFM inverters under ASGFs. Positive-, negative-, and zero-sequence networks are then established to model the system, and a key assumption regarding voltages at fault locations and the X/R ratio of AVI is rigorously validated through quantitative analysis. Based on this verified assumption and the derived sequence networks, the voltage support capability is systematically analyzed and visualized using phasor diagrams. Furthermore, the optimal phase angle of the AVI is derived to maximize the voltage support capability, and its influence on transient stability margin is analyzed. Ultimately, experimental results demonstrate the effectiveness of the presented AVI, as well as the key assumption, the voltage support capability analysis and the transient stability margin evaluation for GFM inverters under ASGFs.

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Publication Details

Journal
IEEE Transactions on Industrial Electronics
Published
2025-10-02
DOI
https://doi.org/10.1109/tie.2025.3603063
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21
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article

Voltage Support Capability Analysis for Grid-Forming Inverters With Adaptive Virtual Impedance Under Asymmetrical Grid Faults

Yunwei Li, Han Zhang, Rui Liu
1 citations
IEEE Transactions on Industrial Electronics
Microgrid Control and Optimization
1.21
article

Voltage Support Capability Analysis for Grid-Forming Inverters With Adaptive Virtual Impedance Under Asymmetrical Grid Faults

Yunwei Li, Han Zhang, Rui Liu
article en
1 citations

Abstract

Current grid codes require grid-forming (GFM) inverters to provide voltage support capability during grid faults to ensure stability and resilience of power systems. While research on voltage support capability for GFM inverters with adaptive virtual impedance (AVI) under symmetrical grid faults has been conducted, its findings cannot be directly applied to asymmetrical grid faults (ASGFs). This limitation arises because ASGFs introduce negative- and zero-sequence networks, significantly complicating the theoretical analysis. Given the prevalence and complexity of ASGFs, a comprehensive theoretical framework for analyzing voltage support capability is essential. To address this gap, this article first presents the implementation of AVI for GFM inverters under ASGFs. Positive-, negative-, and zero-sequence networks are then established to model the system, and a key assumption regarding voltages at fault locations and the X/R ratio of AVI is rigorously validated through quantitative analysis. Based on this verified assumption and the derived sequence networks, the voltage support capability is systematically analyzed and visualized using phasor diagrams. Furthermore, the optimal phase angle of the AVI is derived to maximize the voltage support capability, and its influence on transient stability margin is analyzed. Ultimately, experimental results demonstrate the effectiveness of the presented AVI, as well as the key assumption, the voltage support capability analysis and the transient stability margin evaluation for GFM inverters under ASGFs.

IEEE Transactions on Industrial ElectronicsVol. 73(2)
University of Alberta (CA)
Openalex Percentile: Top 16%
Microgrid Control and Optimization
1.21
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