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Record W4205842912 · doi:10.1109/ias48185.2021.9677296

Adaptive Voltage Controller for Permanent Magnet Synchronous Motor in Six-step operation

2021· article· en· W4205842912 on OpenAlexaff
Zisui Zhang, Babak Nahid‐Mobarakeh, Ali Emadi

Bibliographic record

Venue2021 IEEE Industry Applications Society Annual Meeting (IAS) · 2021
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)Voltage controllerDisturbance voltageVoltageVoltage regulatorDropout voltageController (irrigation)Direct torque controlVoltage dividerTorqueVoltage regulationSynchronous motorComputer scienceVector controlVoltage droopEngineeringPhysicsInduction motorControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

An adaptive voltage control scheme in flux-weakening control is proposed for permanent magnet synchronous motor (PMSM). Voltage feedback control is applied to utilize the DC link voltage, and maximum torque range for flux-weakening region is extended. The analysis of voltage controller is carried out, taking into account of modulation delay and non-linear effect of six-step mode limitation, to issue the compensation performance with different voltage feedback gains from difference between the magnitude of voltage vectors. Based on those feedback voltage paths, the gain of voltage controller is optimized as an adaptive value by current vector and voltage angle to lead to a larger torque range and system efficiency in flux-weakening region. Dynamic performance and whole flux-weakening stable operation can be implemented with the proposed adaptive voltage control. Verifications are carried out on a 7kW PMSM drive system to prove the features and effectiveness of the proposed technique.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venue2021 IEEE Industry Applications Society Annual Meeting (IAS)Same topicSensorless Control of Electric MotorsFrench-language works237,207