MétaCan
Menu
Back to cohort
Record W4285099266 · doi:10.18280/jesa.550312

Active Harmonic Filtering for Improving Power Quality of an Electrical Network

2022· article· en· W4285099266 on OpenAlexvenueno aff
M. Benaouadj, Zouhir Boumous, Samira Boumous

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsnot available
Fundersnot available
KeywordsTotal harmonic distortionHarmonicsActive filterTHD analyzerHarmonicDistortion (music)AC powerComputer scienceElectronic engineeringFilter (signal processing)Electronic filterQuality (philosophy)Power (physics)Power electronicsEnergy (signal processing)ElectronicsControl theory (sociology)Electrical engineeringEngineeringControl (management)AcousticsVoltageMathematicsNonlinear distortionPhysicsArtificial intelligenceAmplifier

Abstract

fetched live from OpenAlex

The increase use of polluting equipment, especially in the field of power electronics, has caused appearance of harmonics and deterioration in the supplied current wave quality. For this reason, several harmonic pollution control methods have been developed, and the use of the active filtering presents the most efficient and appropriate solution. This work concerns improving energy quality of an electrical network by using an active parallel filter. To achieve this objective, three essential steps were pursued: identification of the harmonic currents, insertion and control of an active parallel filter, and checking effectiveness of the adopted filtering strategy by comparing the total harmonic distortion (THD) values before and after filtering. The various obtained results prove capacity of the inserted active filter to decontaminate harmonic pollution and consequently improve the energy 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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.281
Teacher spread0.247 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicPower Quality and HarmonicsFrench-language works237,207