Fuzzy Logic-Controlled Virtual Impedance for Improving LVRT Performance in Grid-Forming Inverters
Bibliographic record
Abstract
Power systems are undergoing a significant transformation from conventional fossil fuel-based generation—primarily driven by synchronous generators (SGs)—toward renewable energy resources that are integrated via power electronic inverters, commonly referred to as inverter-based resources (IBRs). A critical requirement for grid-forming (GFM) IBRs is the ability to maintain stable operation during low-voltage conditions, known as low-voltage ride-through (LVRT). To address this challenge, this paper presents a novel control strategy for GFM inverters based on a fuzzy logic-controlled virtual impedance framework. The proposed method aims to improve both AC-side fault current limiting and preserve DC-link stability during grid faults. Unlike conventional approaches that assume fixed virtual impedance parameters, the proposed control dynamically adjusts virtual resistance and reactance in real time using fuzzy inference rules. This enables the inverter to respond effectively to both symmetrical and asymmetrical faults. Additionally, the approach explicitly considers the dynamic behavior of the DClink, a factor often neglected in previous works. The performance of the proposed fuzzy-based virtual impedance is validated on the IEEE 9-bus system in the MATLAB/Simulink environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".