Simultaneous Overvoltage and Overcurrent Mitigation of Grid-Forming Inverters under A Single-Line-Ground Fault
Bibliographic record
Abstract
Single-line-ground fault, which happens at the delta terminal of a$\mathrm{Y}g\Delta$transformer or the ungrounded wye terminal of a$\mathrm{Y}g\mathrm{Y}$transformer in a grid-forming inverter system will cause severe overcurrent and overvoltage simultaneously. However, they are rarely investigated together and mitigated through a control strategy at the same time. In this paper, phase voltages at the point of common coupling (PCC) and inverter output currents during the fault are firstly calculated based on the sequence network of the system. Subsequently, to ride through the fault, the hybrid mitigation strategy based on the virtual negative-sequence and positive-sequence impedance is proposed. The virtual negative-sequence impedance, realized through current feedback control, can not only reduce the overvoltage at healthy phases slightly and equalize them but also reduce inverter fault currents significantly. Besides, its weak overvoltage and strong overcurrent limiting abilities are also analyzed with varying grid short-circuit ratios and fault impedances. To limit the overvoltage, the virtual positive-sequence impedance can be increased during the fault in each control time step until the maximum phase voltage at the PCC is lower than the fault ride-through requirement. Consequently, the proposed mitigation strategy is verified by real-time simulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".