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Record W2079388160 · doi:10.1109/eeeic.2010.5489996

Real-time laboratory implementation results of an active filter

2010· article· en· W2079388160 on OpenAlexaff
Alireza Javadi, G. Olivier, Frédéric Sirois, André Youmssi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInterfacingHarmonicsCompensation (psychology)Power electronicsComputer scienceActive filterConvertersElectronicsThree-phaseAC powerSoftwareFilter (signal processing)Electronic engineeringControl systemControl theory (sociology)Electrical engineeringEngineeringVoltageControl (management)Computer hardware

Abstract

fetched live from OpenAlex

Proliferation of power electronics converters and electronic equipments has dramatically increased electric pollution in electrical distribution power systems. One solution to this problem could be the use of active filters that are capable of injecting distorted currents in order to cancel harmonics coming from non-linear loads for instance, or from any other equipment. Consequently, many theories have been developed to control active filters and then perform compensation of unwanted harmonics. In this paper, an overview of the instantaneous compensation theory is presented. This time domain approach was proposed by Akagi [1] under the name “p-q theory” or “instantaneous power theory”. It is the most widely spread theory for two or three phases and three or four wire systems. Then an appropriate control model for active filters based on the literature review is presented. That model is interfaced with real physical system owing to Opal-RT Software that definitely allows real-time control. Laboratory implementations are realized by using RT-Lab Simulator, and by interfacing it with a three phase 120/240 V system and a three phase non-linear load. The final system constitutes a Hardware in the loop (HIL) application. Real-time tests on an Opal-RT simulator, are shown and commented in the paper.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.612

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)0.0010.000

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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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
Published2010
Admission routes1
Has abstractyes

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