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Record W4205863980 · doi:10.1109/access.2022.3141542

Multiphase Traction Inverters: State-of-the-Art Review and Future Trends

2022· article· en· W4205863980 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSizingInverterTraction (geology)Network topologyCapacitorElectrificationComputer scienceVoltageElectronic engineeringPower (physics)Electrical engineeringAutomotive engineeringEngineeringElectricityComputer networkMechanical engineering

Abstract

fetched live from OpenAlex

Multiphase inverters (MPIs) continue to increase in popularity owing to their compelling features that include enhanced fault-tolerance capability, improved per-phase power handling, and reduced dc-bus capacitor sizing. This article presents a comprehensive review on MPIs and their application in transportation electrification. More specifically, voltage source inverter (VSI) and nine-switch inverter (NSI) are the two MPI topologies reviewed herein, due to their popularity and potential for employment as traction inverters. The state-of-the-art review covers modeling and control techniques, dc-capacitor sizing, modulation strategies, inverter losses, and cost. Promising future trends of MPIs in terms of topologies, switching devices and integrated design are also investigated.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.409

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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