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Record W2123798165 · doi:10.1109/63.844510

An improved control algorithm of shunt active filter for voltage regulation, harmonic elimination, power-factor correction, and balancing of nonlinear loads

2000· article· en· W2123798165 on OpenAlexaff
Ambrish Chandra, Bhim Singh, Kamal Al‐Haddad

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2000
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)Power factorHarmonicsPulse-width modulationThree-phaseVoltage regulationEngineeringInsulated-gate bipolar transistorActive filterVoltageTotal harmonic distortionSwitched-mode power supplyAC powerElectronic engineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

This paper deals with an implementation of a new control algorithm for a three-phase shunt active filter to regulate load terminal voltage, eliminate harmonics, correct supply power-factor, and balance the nonlinear unbalanced loads. A three-phase insulated gate bipolar transistor (IGBT) based current controlled voltage source inverter (CC-VSI) with a DC bus capacitor is used as an active filter (AF). The control algorithm of the AF uses two closed loop PI controllers. The DC bus voltage of the AF and three-phase supply voltages are used as feedback signals in the PI controllers. The control algorithm of the AF provides three-phase reference supply currents. A carrier wave pulse width modulation (PWM) current controller is employed over the reference and sensed supply currents to generate gating pulses of IGBTs of the AF. Test results are presented and discussed to demonstrate the voltage regulation, harmonic elimination, power-factor correction and load balancing capabilities of the AF system.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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
GenreMethods

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

Citations338
Published2000
Admission routes1
Has abstractyes

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