An algorithmic wide-range synchronization system based on a predictive phase locked loop architecture
Bibliographic record
Abstract
An algorithmic wide-range synchronization system for applications in utility and non-utility power systems and power electronics is presented. The synchronization system is based on a predictive phase locked loop (PPLL) architecture. The PPLL is fully adaptive in extracting time variant synchronization information from an input signal. The fundamental component of the input signal is extracted in the form of three equidistant samples using a fundamental sample extractor. The synchronization information includes frequency, amplitude, and phase angle with respect to a reference frame that can be altered so as to allow a real-time phase offset implementation. A set of requirements for utility and non-utility applications is developed. The PPLL can extract the synchronization information from the input signal over a wide range of frequency and amplitude in the presence of disturbances. Two methods are developed to extract the synchronization information. The strengths of each method are exploited so as to achieve high execution speed and low real estate utilization. The mathematical properties of the two methods are presented. The synchronization system is implemented on a field programmable gate array (FPGA). The operating range for frequency and amplitude are from a fraction of Hz to a few kHz and from 4% to 100% of nominal amplitude respectively. The synchronization information is extracted within two cycles of the input signal period under any realistic perturbations in frequency, amplitude, and/or phase angle. The proposed synchronization method is faster, more flexible and more robust than the currently available methods.
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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 | 0.000 |
| 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) | 0.003 | 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".