$\alpha \alpha \beta$-Based Fast and Accurate Frequency and ROCOF Measurement in Power Systems
Bibliographic record
Abstract
The frequency and rate of change of frequency (ROCOF) signals acquired from phasor measurement units (PMUs) are instrumental in the security monitoring and feedback control of power grids, necessitating minimal response time and high precision. Existing algorithms rarely achieve a simultaneous balance between superior noise filtering performance and rapid response time when measuring frequency and ROCOF. To address this problem, this article introduces an innovative algorithm that integrates$\alpha \beta \gamma$series filters. The proposed algorithm leverages an$\alpha \alpha \beta$(aab) filter, which combines an$\alpha$filter and an$\alpha \beta$filter, and the reference P-class PMU algorithm from the IEC/IEEE 60255.118.1-2018 standard file. By providing an original mathematical modeling approach to aab parameter acquisition, this filter effectively amalgamates the advantages of the two foundational filters. Extensive experimental simulations demonstrate that our proposed algorithm has exceptional performance across various metrics of frequency and ROCOF measurement, achieving results that compare favorably with other advanced algorithms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".