Controlling Grid-Forming Inverters to Meet the Negative-Sequence Current Requirements of the IEEE Standard 2800-2022
Bibliographic record
Abstract
As an integral component of power systems dominated by inverter-based resources (IBRs), grid-forming (GFM) inverters must ride through low voltages. During an asymmetrical low-voltage ride-through (LVRT) condition, the recently approved IEEE Standard 2800-2022 requires that all IBRs absorb negative-sequence reactive current as a function of the voltage at the IBR's terminal. However, existing GFM control methods either suppress the negative-sequence component of the fault current or leave it uncontrolled. To address this issue, this article develops a control scheme that makes GFM-IBRs absorb reactive current in the negative-sequence circuit while they regulate the voltage in the positive-sequence circuit. The developed control system includes a new adaptive virtual impedance-based current-limiting scheme to limit the inverter's current during both initial transients and steady-state fault conditions. To meet the maximum phase current utilization requirement of the IEEE Std. 2800-2022, the paper also develops an adaptive sequence current division scheme. PSCAD/EMTDC simulations of a CIGRE transmission network benchmark supplied primarily by IBRs verify the compliance of the proposed control system with the IEEE Std. 2800-2022.
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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".