Comparative Analysis of Negative Sequence Behavior in Grid-Following and Grid-Forming Inverters: Modeling, Control, and Protection
Bibliographic record
Abstract
Grid-forming (GFM) controls are expected to enhance the stability of power systems with high penetration of inverter-based resources (IBRs). However, during unbalanced grid conditions, GFM controls alter the magnitude and angle of the negative sequence current contributed by IBRs, differing from grid-following (GFL) controls and synchronous generators. This behavior may not be in line with fault ride-through (FRT) requirements and cause maloperation of certain protection elements. This paper clarifies the fundamentals and impacting factors of GFL and GFM inverters in the negative sequence system using phasor-domain analytical models. A simplified impedance computation method for GFM inverters is proposed, and FRT control solutions for GFM and GFL inverters are implemented for balanced positive sequence control (BPSC) and positive and negative sequence control (PNSC) schemes. Comparative studies using the IEEE PSRC D29 system reveal that the magnitude and angle of negative sequence impedance are primarily determined by the inner voltage control parameters under the GFM BPSC scheme. Depending on the settings, GFM inverters can provide significant negative sequence reactive current under the BPSC scheme as opposed to GFL inverters. On the other hand, IBRs should still rely on PNSC strategies to regulate the negative sequence system response appropriately and in coordination.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".