Variable Window Size Moving Average Filter for Phase-Locked-Loop Synchronization
Bibliographic record
Abstract
Efficient grid synchronization is crucial for integrating renewable energy sources and Flexible AC Transmission systems (FACTs) into power grids. This paper addresses the challenges faced by Synchronous Reference Frame Phase-Locked Loops (SRF-PLLs) in harmonic-polluted grids and proposes a novel solution employing Moving Average Filters (MAFs). The conventional MAF-PLL with a fundamental period window size provides harmonic rejection but slows down the dynamic response. To enhance MAF-PLL performance under harmonic-polluted grid conditions, we introduce a Variable Window Size (VWS) MAF. The proposed VWS-MAF adapts its window size based on the dominant frequency of oscillation in the dq frame, determined using Short-Time Fourier Transform (STFT). The proposed method ensures minimum window size, based on grid conditions, which improves the dynamic response while maintaining harmonic rejection capabilities. The proposed method offers improved adaptability and promising performance in elimination of integer harmonics, DC offset, interharmonics, and negative sequence component. Simulation and experimental studies are presented that demonstrate the effectiveness of VWS-MAF, positioning it as a noteworthy advancement in PLL technology for robust grid synchronization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".