MétaCan
Menu
Back to cohort
Record W2965044809 · doi:10.1109/isie.2019.8781515

Hybrid Single Phase Wide Range Amplitude and Frequency Detection with Fast Reference Tracking

2019· article· en· W2965044809 on OpenAlexaff
Luccas M. Kunzler, Luiz A. C. Lopes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsSynchronizingPhase-locked loopComputer sciencePhase (matter)AmplitudePhase detectorElectronic engineeringPhase distortionAmplifierPower (physics)Control theory (sociology)Bandwidth (computing)Electrical engineeringTelecommunicationsEngineeringTransmission (telecommunications)JitterPhysicsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

Amplitude, phase and frequency detection is key for synchronizing different AC sources. The most common and ever growing largely, usage for this technique is in grid interfaces for renewable sources. The determination of these variables are also useful for other power electronics applications, such as in Hybrid Power Amplifier (HPA), especially if it is digitally controlled. Several three-phase applications use variations of the Phase-Locked Loop (PLL) technique to determine the phase of the signals in order to apply the Clarke's Transformation to reduce the complexity of the system. Single-phase systems are more challenging since they require additional and more complex techniques to determine the phase. Usually, both single and three-phase systems are designed for a single and known frequency, usually the grid's frequency. However, a wider range of frequencies is necessary for other applications such as HPAs. In this paper, a hybrid solution for a single-phase signal amplitude and frequency detection with fast dynamics and wide input variation without prior knowledge of the frequency is proposed and evaluated experimentally. This solution enables the use of proper frequency, amplitude and phase values at the input of HPAs in order to improve the output quality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.191
Teacher spread0.182 · 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 teacher head, 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207