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Record W2118154735 · doi:10.1109/tia.2010.2049814

Development and Testing of a New Controlled Wavelet-Modulated Inverter for IPM Motor Drives

2010· article· en· W2118154735 on OpenAlexaff
S. A. Saleh, M.A. Rahman

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2010
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInverterVoltageControl theory (sociology)Induction motorComputer scienceTorqueMotor driveDigital signal processorVector controlDigital signal processingEngineeringElectronic engineeringElectrical engineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper presents the development, implementation, and performance testing of a novel resolution-level vector controller (RLVC) for a wavelet-modulated (WM) inverter-fed interior permanent-magnet (IPM) motor drive system. The RLVC is designed to adjust the output voltage of the WM inverter in response to changes in the load torque and command speed. The adjustments in the WM inverter output voltage is accomplished through changing the maximum value of the scale <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</i> that is required to generate the WM inverter switching signals. The complete IPM motor drive system incorporating the RLVC is successfully implemented in real time using a digital signal processor board ds1104 for a laboratory 1-hp IPM motor. The performances of the proposed RLVC IPM motor drive system are investigated at different dynamic operating conditions, including sudden changes in the command speed and load torque. The simulated and experimental performance results show stable, fast, and accurate adjustments of the inverter output voltage in response to load and speed changes in the tested IPM motor drive system.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.671

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 head0.027
GPT teacher head0.237
Teacher spread0.210 · 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
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

Citations44
Published2010
Admission routes1
Has abstractyes

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