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Record W4312393763 · doi:10.1109/tia.2022.3220137

Adaptive Voltage Controller for Permanent Magnet Synchronous Motor in Six-Step Operation

2022· article· en· W4312393763 on OpenAlexaff
Zisui Zhang, Sumedh Dhale, Babak Nahid‐Mobarakeh, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl theory (sociology)Voltage controllerController (irrigation)VoltageDirect torque controlTorqueVoltage regulatorVoltage regulationSynchronous motorDisturbance voltagePermanent magnet synchronous generatorEngineeringDropout voltageComputer scienceVoltage droopPhysicsInduction motorElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

An adaptive voltage controller scheme for flux-weakening control under the six-step operation of the Permanent Magnet Synchronous Machine (PMSM) drives is propose in this paper. In the presented technique, the voltage feedback control is applied to fully utilize the DC link voltage, consequently extending the maximum torque range in the flux-weakening region. The behavior of a voltage controller is analyzed under the effect of modulation delay and nonlinear-gain characteristics of six-step operation. Subsequently, a compensatory control action is generated with varying voltage feedback gains based on the magnitude difference between the voltage vectors. This action maximizes the gain of the voltage controller as a function of current vector magnitude and the voltage angle to achieve a greater torque range and higher efficiency in the flux-weakening zone. Features and effectiveness of the proposed technique were verified by simulation and experimental results on a PMSM drive platform.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.826

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 integrity0.0000.001
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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2022
Admission routes1
Has abstractyes

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