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Record W4320496312 · doi:10.3390/aerospace10020168

Enhanced Starting Control Scheme for PMM-Based Starter/Generator System for MEA

2023· article· en· W4320496312 on OpenAlexaff
Mohamed A. A. Mohamed, Seang Shen Yeoh, Jason Atkin, Ahmed M. Diab, Mohsen Khalaf, Serhiy Bozhko

Bibliographic record

VenueAerospace · 2023
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Toronto
FundersCleansky
KeywordsVoltageGenerator (circuit theory)Control theory (sociology)MATLABPower (physics)Range (aeronautics)Voltage regulatorEngineeringControl systemAutomotive engineeringComputer scienceControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

A control approach for aircraft Starter/Generator (S/G) with Permanent Magnet Machine (PMM) operating in Flux Weakening (FW) mode is presented. The proposed strategy helps the previous approaches which are adopted for the Variable Voltage Bus (VVB) or Voltage Wild Bus (VWB) concept for an aircraft Electric Power System (EPS), to cover a wide speed range in motoring and generation modes. Compared to prior works, the proposed control approach adjusts the q-axis reference voltage with a single current regulator, and the maximum available voltage provided by the converter is used to evaluate the d-axis voltage. By adopting the proposed approach, the DC bus voltage can be fully utilized, increasing aircraft efficiency by allowing the S/G system to operate at a wide range of speeds. The results of the analytical design and the performance of the system were verified by time-domain simulations using MATLAB/Simulink and experiments and compared to the conventional method.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

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 head0.017
GPT teacher head0.224
Teacher spread0.208 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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