Improving Performance of Three-Phase MAF-PLL Under Asymmetrical DC-Offset Condition
Bibliographic record
Abstract
Synchronization is a critical aspect of integrating renewable energy sources and inverter-based power plants into the electrical grid. Phase-Locked Loops (PLLs) are widely used for this purpose, providing rapid and accurate phase and frequency estimation. In PLLs, the Moving Average Filter (MAF) is commonly employed to extract the fundamental grid voltage component, particularly in the presence of harmonic distortions. Traditional PLLs with a full-cycle time-window MAF perform well in grids with sinusoidal voltage waveforms and DC offsets. However, this approach sacrifices the speed of dynamic response due to the extended time window. In this paper, we introduce a novel approach to address this trade-off. Our method involves reducing the MAF’s time window to one cycle by incorporating a delay operator, effectively reducing model complexity and runtime by 50%. Through comprehensive simulations and experimental scenarios, we demonstrate the practical advantages of the proposed method. Comparison of the proposed approach is provided with existing algorithms in the literature, which illustrate its effectiveness in terms of mitigating PLL oscillations in the presence of DC offsets and other non-ideal grid conditions while achieving a 50% improvement in the execution speed. Therefore, the contribution of this paper is in the field of grid synchronization by providing a balanced solution that enhances dynamic response without compromising DC-offset rejection. The proposed method can improve the stability and efficiency of grid-connected systems involving renewable energy sources and inverter-based power plants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".