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Record W2115539864 · doi:10.1109/tpel.2002.800963

Implementation of a DSP based real-time estimator of induction motors rotor time constant

2002· article· en· W2115539864 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2002
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversité du Québec à Trois-RivièresHydro-Québec
FundersHydro-Québec
KeywordsControl theory (sociology)Squirrel-cage rotorInduction motorRotor (electric)EstimatorTorqueMATLABConstant (computer programming)Digital signal processingComputer scienceAC motorDigital signal processorVoltageEngineeringElectronic engineeringPhysicsMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Implementation of ac drives insensitive to parameter variations is an important need in the field of high performance drives. For drives controlled by the indirect rotor flux oriented control method (IRFOC), the rotor time constant (/spl tau//sub r/ = L/sub r//R/sub r/) exerts a dominant role in the loss of dynamic performance and its variation results in an undesirable coupling between flux and torque of the machine. This paper presents a new scheme for on-line estimation of rotor time constant using dq representation of the model in the stationary reference frame and measurements of accessible motor variables only (voltages, currents and speed). The estimator is tested by simulation in the MATLAB/SIMULINK environment and validated experimentally on a 1/4 hp squirrel cage motor and a 1/4 hp wound rotor motor with implementation on a TMS320C31 digital signal processor.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.216
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