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Record W2316577607 · doi:10.1109/ecce.2014.6953542

Variable-speed IGBT gate driver with loss/overshoot balancing for switching loss reduction

2014· article· en· W2316577607 on OpenAlexaff
Alexey Sokolov, Diego Mascarella, G. Joós

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsInsulated-gate bipolar transistorGate driverOvershoot (microwave communication)VoltageSwitching timePulse-width modulationReduction (mathematics)Electrical engineeringDriver circuitControl theory (sociology)Computer scienceElectronic engineeringEngineeringControl (management)

Abstract

fetched live from OpenAlex

This paper introduces a new method of IGBT switching loss reduction on the system level, while leaving the PWM scheme completely unchanged. The switching loss reduction is achieved by designing an IGBT gate driver that dynamically sets the IGBT gate current depending on feedback signals from IGBT current, IGBT voltage, phase load current, and DC link voltage if it is not constant in the application. Factors influencing switching losses will be demonstrated for two types of output driver stages: one with discrete switching speed setting and one with continuously variable switching speed. Comparing to other gate driver types with or without feedback aimed at keeping constant dv/dt, di/dt, and overshoots of IGBT voltage and current, the proposed gate driver not only ensures the operation of an IGBT in the safe operating area (SOA), but also improves the SOA utilization density by tracking the programmed voltage and current limits using peak-detection circuitry while minimizing the switching losses. Adaptive feedback control algorithms have been developed and verified by simulations.

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 score0.277
Threshold uncertainty score0.862

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 head0.008
GPT teacher head0.197
Teacher spread0.190 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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