Investigation and Graphical Design of Frequency Limiter in PLL-Synchronized Inverters for Enhancing Synchronization Stability Under Low-Voltage Grid Fault
Bibliographic record
Abstract
Phase-locked loop (PLL)-synchronized inverters, characterized by low inertia and weak damping, are prone to loss of synchronization (LOS) during and after low-voltage grid faults. To mitigate LOS, existing studies have primarily focused on carefully designing the fault clearance angle (FCA) to enhance postfault synchronization stability. However, the PLL frequency limiter (FL), which is essential in practice and is crucial to transient stability, is often neglected in state-of-the-art research. This article investigates the effect of FL on system synchronization stability throughout the entire fault period. For systems stable during low-voltage grid faults, an overly strict FL can deteriorate synchronization stability. On the other hand, for systems experiencing LOS without an FL, introducing a strict FL slows phase evolution and enhances synchronization stability after the fault is cleared. Furthermore, the article investigates the relationship between the FL and FCA based on phase portraits and the domain of attraction, and proposes a graphical FL design method to eliminate the risk of LOS after fault clearance. The proposed method ensures that the PLL-synchronized inverter system maintains synchronization stability after fault clearance, regardless of the FCA. Finally, these findings are validated through experimental results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".