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Record W2160529147 · doi:10.1109/ccece.2006.277478

A Peak Detector for Multi-Rate Phase Locked Loop and Sequence Detector Combination for Utility AC Power Applications

2006· article· en· W2160529147 on OpenAlexafffund
Hamid Timorabadi, E. Chen, F.P. Dawson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Toronto
FundersCMC Microsystems
KeywordsFast Fourier transformDetectorPhase detectorPhase-locked loopComputer scienceAmplitudeEstimatorSynchronization (alternating current)Electronic engineeringPhase synchronizationControl theory (sociology)Phase detector characteristicAlgorithmPhase noiseEngineeringElectrical engineeringPhysicsMathematicsVoltageTelecommunicationsOptics

Abstract

fetched live from OpenAlex

Synchronization is of concern in AC power systems. Existing synchronization algorithms are hardware intensive. A single phase synchronization scheme referred to as a multi-rate phase locked loop (MPLL) has been reported. The stability of the MPLL is influenced by amplitude variations. An amplitude estimation module based on a fast Fourier transform (FFT) algorithm and an automatic gain control (AGC) are employed to decouple amplitude variations from phase variations. The implementation of the FFT-based amplitude-estimator is complex and hardware intensive. A peak detector module that utilizes only two multiplexers and one comparator is proposed to replace the FFT-based amplitude-estimator. A real-time positive sequence detector is also combined with the MPLL in order to allow process of the three-phase signals. The overall system is implemented and compared for both FFT-based and peak detector approaches and indicates a 60 dB immunity to impulse noise and harmonic contamination and a 20 dB dynamic range

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.032
GPT teacher head0.288
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2006
Admission routes2
Has abstractyes

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