MétaCan
Menu
Back to cohort
Record W2170577819 · doi:10.1109/pesc.2004.1355339

Wavelet-based dynamic voltage restorer for power quality improvement

2004· article· en· W2170577819 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551) · 2004
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDisturbance voltageComputer scienceVoltageControl theory (sociology)WaveletWavelet transformFilter (signal processing)Electronic engineeringVoltage regulationEngineeringVoltage optimisationControl (management)Artificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a novel method for power quality improvement employing wavelet analysis. A wavelet filter bank is used to detect and classify various disturbances experienced by the voltage on the load side. A dynamic voltage restorer is used to compensate the voltage under disturbance conditions and maintain the rated load voltage. The input and the output voltages of the DVR are both controlled by the wavelet filter bank outputs. The approximations control the input voltage to the AC-DC converter. The control pulses of the DC-AC inverter are controlled by the details. Different types of disturbances are simulated to test the performance of the proposed control scheme. The proposed wavelet based control scheme successfully provided fast and accurate voltage restoration following any form of transient disturbances.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.018
GPT teacher head0.274
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