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Record W1913679638 · doi:10.1109/ecce.2015.7310064

A comparative study of various methods of IM's rotor resistance estimation

2015· article· en· W1913679638 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMRASRotor (electric)Control theory (sociology)TorqueGeneralizationComputer sciencePower (physics)Estimation theoryInduction motorAC powerControl engineeringEngineeringMathematicsAlgorithmArtificial intelligenceVector controlVoltagePhysics

Abstract

fetched live from OpenAlex

Various MRAS-based estimation schemes of IM's rotor resistance has been proposed. Three simple rotor resistance identification methods of an induction motor drive are presented and compared. Two of the three techniques namely, electromagnetic torque equation, and reactive power equations are based on the generalization of existing methods. Authors proposed the third method, namely, active power equation which is an improvement over the existing schemes. Although all three schemes estimate the rotor resistance effectively, the proposed technique is superior to others in providing faster estimation approach. The proposed method will be analyzed along with other previously proposed MRAS based methods, and the advantage and limitations of each will be discovered. Simulation and experimental results confirmed that the rotor resistance estimation by active power equation is much faster than the other mentioned methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.335

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 head0.046
GPT teacher head0.342
Teacher spread0.296 · 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

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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