MétaCan
Menu
Back to cohort
Record W2539910762 · doi:10.1109/ias.1998.730324

A novel single pulse and PWM VAr compensator for high power applications

2002· article· en· W2539910762 on OpenAlexaff
Alireza Bakhshai, G. Joós, Prashant Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsPulse-width modulationTransformerElectronic engineeringConvertersInverterComputer scienceVoltageControl theory (sociology)Power (physics)Switching frequencyEngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

Although pulse-width-modulated (PWM) high power GTO-based voltage source inverters have unique control features, obstacles to their use in high power applications include reduced efficiency, and lower inverter utilization. This paper presents a low switching frequency pulse-width-modulation (PWM) technique that can be used in conjunction with the principle of harmonic neutralization. This allows the combination of the advantages of both multi-pulse and PWM GTO-based voltage source converters. The proposed converter can be operated in either the single pulse or the PWM modes. In the PWM mode, a special low switching frequency (3 pu) space vector strategy is used to maximize voltage utilization while maintaining a transfer linear characteristic. In the six-step mode, the switching frequency, and thus the switching losses are minimized. In addition the paper proposes an alternative to complicated zig-zag connected phase-shifting transformers used in standard implementations. The power structure and control methods are described and analyzed. Experimental results validate and demonstrate the performance of the proposed techniques.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.196
Teacher spread0.173 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicMultilevel Inverters and ConvertersFrench-language works237,207