Simultaneous Overvoltage and Overcurrent Mitigation Strategy of Grid-Forming Inverters Under a Single-Line-To-Ground Fault
Bibliographic record
Abstract
A single-line-to-ground (SLG) fault occurs at the ungrounded side of a transformer (e.g., wye-grounded-delta, wye-grounded-wye, or wye-grounded-wye-grounded through a large impedance transformer) in a grid-forming inverter will cause severe physical overvoltage and inverter output overcurrent simultaneously. Utility distribution grid has been facing increasing challenges related to this in recent years. However, this issue has not been well investigated and addressed by state-of-the-art control schemes. To fill this important gap, this article first proposes a negative-sequence voltage compensation method to equalize magnitudes of healthy phase voltages under SLG faults, which helps limit the healthy phase voltages concurrently. Subsequently, a virtual impedance with a simultaneous overvoltage and overcurrent limiting capability is proposed to atomically mitigate the simultaneous overvoltage and overcurrent. Additionally, the well-known current limiting factor in the inner current control loop with a high bandwidth is adopted to limit the transient overcurrent. Simulation and experimental results reveal that the simultaneous overvoltage and overcurrent issues under SLG faults can be addressed by the proposed control strategies. Moreover, the effectiveness of the current limiting under other fault types with the proposed strategy is also confirmed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".