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Record W2104360837 · doi:10.1109/pccon.2007.372966

A Three-Phase Frequency Adaptive Digital Phase Locked Loop for Measurement, Control, and Protection in Power Systems

2007· article· en· W2104360837 on OpenAlexaff
Hamid Timorabadi, F.P. Dawson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhase-locked loopPLL multibitHarmonicsPhase detectorControl theory (sociology)Electronic engineeringAmplitudeNoise (video)Phase (matter)Phase frequency detectorDetectorSynchronization (alternating current)Power (physics)Computer sciencePhase noiseEngineeringPhysicsVoltageElectrical engineeringTopology (electrical circuits)Charge pumpOptics

Abstract

fetched live from OpenAlex

A three-phase phase locked loop (3-Phi PLL) for control and protection in power system applications is presented. The 3-Phi PLL is a frequency adaptive digital system that consists of a positive sequence detector and a single-phase predictive phase locked loop (PPLL). The 3-Phi PLL is fully adaptive in extracting frequency, amplitude, and phase angle in time variant systems. The modeling and mathematical properties of the positive sequence detector and the PPLL are presented. The 3-Phi PLL can accommodate a wide range of frequency and amplitude variations in the presence of noise and disturbances. The 3-Phi PLL is implemented on a field programmable gate array (FPGA). A number of analog-to-digital converters (ADCs) have been evaluated for this application. A successive approximation type ADC offers the best tradeoff between resolution and sampling frequency. The synchronization information is extracted within two cycles of the input signal period. The operation of the 3-Phi is verified for balanced and unbalanced loads in the presence of noise, harmonics, and DC-offsets over a frequency range of a fraction of Hz to a few kHz and over an amplitude range of about 3% to 120% of the nominal voltage.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.514

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 head0.020
GPT teacher head0.226
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations8
Published2007
Admission routes1
Has abstractyes

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