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Record W1531187373 · doi:10.1109/pesc.2006.1711862

Direct AC main current control of shunt active power filters — Feasibility and performance

2006· article· en· W1531187373 on OpenAlexaff
Guangzhu Wang, Guibin Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAC powerControl theory (sociology)ControllabilityCurrent (fluid)HarmonicDirect currentActive power filterActive filterVoltageShunt (medical)Computer scienceHarmonic analysisEngineeringElectronic engineeringControl (management)Electrical engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

Harmonic and reactive current control is a critical technique in the conventional shunt Active Power Filter (APF) control. Recently years, another control scheme — the direct ac main current control is emerging, where the harmonic and reactive current detection module is no longer needed. This paper proves theoretically that the current controllability of the direct ac main current scheme (DCS) is the same as that of the conventional control scheme (CCS) that employs a sophisticated harmonic and reactive current detection module. Based on this finding, a simple and high performance direct ac main current scheme has been developed. Further theatrical analysis shows the harmonic and reactive current detection module in the conventional module just functioning as a forward feeding in the proposed DCS APF control, and the fluctuation of the compensated load is just a disturbance in dc voltage control loop of the proposed control schemes. The simulation results show that discarding the harmonic and reactive current detection module does not sacrifice the steady-state control accuracy. A prototype was developed to demonstrate the feasibility and performance of the proposed APF control scheme, and the above findings are well supported by the experimental results.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.350

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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