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Record W3197770039 · doi:10.1109/tpel.2021.3108667

High-Quality PWM Scheme for High-Speed Electric Drives

2021· article· en· W3197770039 on OpenAlexaff
Chenhui Zhang, Marius Takongmo, John Salmon

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPulse-width modulationInductorInductanceInverterVoltageControl theory (sociology)HarmonicEngineeringElectronic engineeringPhysicsElectrical engineeringComputer scienceAcousticsControl (management)

Abstract

fetched live from OpenAlex

A PWM scheme is presented for 3-phase high-speed electric drives using coupled inductors with a very low series output inductance that generate multi-level output voltages with an elevated PWM frequency. Two inverter legs are used in each phase, connected in parallel using coupled inductors with magnetic cross-coupling. The PWM switching of the inverter legs are controlled using carrier signals 180 apart. Rather than changing these carrier signals on the zero crossovers of the 3-phase reference signals to improve the PWM line voltage, the reference signals are modified instead: enhanced PWM control. This approach allows the use of compensation signals to eliminate flux jumps in the magnetic cores commonly associated with carrier switching. Hence, the flux in the magnetic cores follows predictable patterns, with the peak flux reduced considerably. This, in turn, allows the inductor design to be reduced in size and weight. The enhanced PWM control described is also shown to reduce the load current THDf and the load voltage harmonic volt-seconds. The feasibility of the enhanced PWM control is verified using both simulations and experimental results of a 10kW (208V/28A, 300Vdc) laboratory prototype.

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 categoriesMeta-epidemiology (narrow)
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 score0.865
Threshold uncertainty score1.000

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 head0.239
Teacher spread0.226 · 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.

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

Citations13
Published2021
Admission routes1
Has abstractyes

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