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Record W2558612988 · doi:10.1109/isie.2016.7744998

Reduction of dead-time effect on the common mode voltage of an open-end winding machine

2016· article· en· W2558612988 on OpenAlexaff
Nazli Kalantari, Luiz A. C. Lopes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsHarmonicsDead timeInverterCommon-mode signalVoltageControl theory (sociology)Power (physics)Polarity (international relations)Reduction (mathematics)Computer scienceSequence (biology)EngineeringElectrical engineeringPhysicsControl (management)MathematicsDigital signal processing

Abstract

fetched live from OpenAlex

Open-end winding (OEW) three-phase drives with dual inverter configuration present high voltage gain, which is very valuable for electric vehicle (EV) applications. However, they can lead to zero-sequence currents and common mode voltages (CMV). A solution for the aforementioned problems is to control the inverters with only odd or even space vectors (SVs). Nonetheless, during dead-times the actual SVs of the inverters vary with the polarity of the load currents which can lead to short pulses in the CMV. First, this paper discusses an approach for determining the actual SVs of the inverters during the dead-times. Based on this, a strategy for selecting the sequence of SVs as a function of the load current polarities for eliminating the spikes in the CMV is proposed. The impact of the proposed strategy on the load voltage and current harmonics as well as on the switch power losses is investigated. The findings of this work are supported by simulation 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.485

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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