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Record W1996504632 · doi:10.1109/pesc.2007.4342146

A Novel Hybrid Series Active Filter for Power Quality Compensation

2007· article· en· W1996504632 on OpenAlexafffund
Ab. Hamadi, S. Rahmani, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsHarmonicsAC powerActive filterPower factorHarmonicElectronic engineeringControl theory (sociology)Electrical impedanceHarmonic analysisEngineeringCompensation (psychology)Electronic filterVoltage optimisationComputer scienceVoltageElectrical engineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper presents a new hybrid series active filter (HSAF) for compensating voltage/current harmonic, type of loads, reactive power and damping of resonant. It is a combined system of shunt passive filter (SPF) and a series active filter (SAF). The integration of passive and active power filters is very important for reducing power rating of the active part. The novel SPF uses minimum component count. The principle of the SPF is to provide a high impedance at the fundamental frequency and a very low-impedance higher harmonics generated by the load, thus compensating effectively all harmonics and reactive power to improve power factor. The SAF presents high impedance during harmonics compensation what forces the harmonics to flow through the SPF and compensates the voltage harmonic and the reactive power. the control of the SAF is based on Synchronous Reference Frame (SFR) method. The simulation results are discussed, and the performance of the topology is therefore evaluated.

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)0.000
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 score0.026

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.055
GPT teacher head0.300
Teacher spread0.245 · 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

Citations35
Published2007
Admission routes2
Has abstractyes

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