Transient Stability Analysis and Coordinated Phase Control Method for Multiparallel PLL-Synchronized Inverters During Grid Fault
Bibliographic record
Abstract
To utilize renewable energies, such as solar energy, distributed power generation systems are widely used, where multiple string inverters are parallelly connected to the grid and often adopt the phase-locked loop (PLL) for grid synchronization. However, under severe voltage sags caused by grid faults, the PLL-synchronized inverter system is susceptible to transient instability, which manifests as loss of synchronization. Although such transient instability caused by interactions between the inverter and the grid has been well studied in state-of-the-art research, the instability due to interactions among different inverters is not fully considered. Thus, to investigate this issue, this article establishes the modeling of ann-parallel PLL-synchronized inverter system for transient stability analysis while considering the parameter difference. It is revealed that the inter-inverter interaction introduces the transient instability risk, even though every grid-connected inverter is separately designed to keep synchronization during the grid fault. Subsequently, this article proposes a coordinated phase control method to enhance the transient stability of the multiparallel inverter system during grid faults. Finally, utilizing the RT-LAB OP5707XG platform, a real-time simulation model of a 3-parallel PLL-synchronized inverter system is established and tested to verify the theoretical analysis and the proposed method.
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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) | 0.002 | 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".