Grid Impedance Online Estimation Based on Intrinsic Dynamic Response for Grid-Forming Inverter Under State of Synchronization Instability

The grid-forming inverter (GFM), which can generate frequency and voltage independently, is a necessary grid-connected device for new energy-high-penetration grid. But there is a risk of deviation from the steady-state operating point and thus lose synchronization in the presence of large disturbances due to the grid faults. The active and precise allocation of a new stable point under large disturbance requires the online estimate of grid impedance. However, previous grid impedance estimation methods rely on small-signal systems, which do not allow for real-time estimation in the presence of large disturbances. To fill this gap, this paper establishes the kinetic equations for the voltages and currents at the point of common coupling (PCC) in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α-β</i> frame according to the natural response excited by large disturbances, and propose a non-intrusive online estimation method based on this model to fit the large-signal grid impedance using the recursive least-squares (RLS). The experimental results show that the proposed method can estimate the impedance within 0.3 s after the grid impedance change, and the estimation accuracy is 95%. When the GFM is losing synchronization, the proposed method can still estimate the grid impedance, which can then be used to actively configure a steady-state operating point.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Power Systems
Published
2025-05-27
DOI
https://doi.org/10.1109/tpwrs.2025.3574211
Citations
2
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
1.06
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Grid Impedance Online Estimation Based on Intrinsic Dynamic Response for Grid-Forming Inverter Under State of Synchronization Instability

Hankang Tian, Tingyi Jiang, Jiaqi Yu, Yijun Wang et al.
2 citations
IEEE Transactions on Power Systems
Power Systems and Renewable Energy
1.06
article

Grid Impedance Online Estimation Based on Intrinsic Dynamic Response for Grid-Forming Inverter Under State of Synchronization Instability

Hankang Tian, Tingyi Jiang, Jiaqi Yu, Yijun Wang, Jingrong Yu, Chen Peng, Wenhao Yang
article en
2 citations

Abstract

The grid-forming inverter (GFM), which can generate frequency and voltage independently, is a necessary grid-connected device for new energy-high-penetration grid. But there is a risk of deviation from the steady-state operating point and thus lose synchronization in the presence of large disturbances due to the grid faults. The active and precise allocation of a new stable point under large disturbance requires the online estimate of grid impedance. However, previous grid impedance estimation methods rely on small-signal systems, which do not allow for real-time estimation in the presence of large disturbances. To fill this gap, this paper establishes the kinetic equations for the voltages and currents at the point of common coupling (PCC) in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α-β</i> frame according to the natural response excited by large disturbances, and propose a non-intrusive online estimation method based on this model to fit the large-signal grid impedance using the recursive least-squares (RLS). The experimental results show that the proposed method can estimate the impedance within 0.3 s after the grid impedance change, and the estimation accuracy is 95%. When the GFM is losing synchronization, the proposed method can still estimate the grid impedance, which can then be used to actively configure a steady-state operating point.

IEEE Transactions on Power SystemsVol. 40(6)
Central South University (CN), Changsha University (CN)
Climate action
Openalex Percentile: Top 21%
Power Systems and Renewable Energy
1.06
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.