MétaCan
Menu
Back to cohort
Record W2005626999 · doi:10.1109/tia.2013.2261972

A Novel and Fundamental Approach Toward Field and Damper Circuit Parameter Determination of Synchronous Machine

2013· article· en· W2005626999 on OpenAlexaff
Kaushik Mukherjee, K. Lakshmi Varaha Iyer, Xiaomin Lu, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2013
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFlux linkageDamperEquivalent circuitField (mathematics)VoltageSynchronous motorReference frameComputer scienceEstimation theoryElectronic engineeringControl theory (sociology)Frame (networking)EngineeringControl engineeringAlgorithmMathematicsElectrical engineeringMechanical engineeringInduction motorArtificial intelligence

Abstract

fetched live from OpenAlex

In this era of advanced computing where complex algorithms and expensive approaches are used to determine the machine parameters of a synchronous machine (SM), this paper proposes a novel, economical, and yet fundamental approach toward estimation of the d- and q-axis fields and damper circuit parameters of a low-/medium-power wound-field SM. The proposed novel methodology employs fundamental voltage, current, and flux linkage relationships of the three-phase wound-field SM in a-b-c reference frame theory. First, the proposed methodology has been explained in detail using analytical equations and then employed to determine the aforementioned parameters of a small laboratory SM. Other equivalent circuit parameters have been determined using conventional tests. Further validation of the proposed methodology was performed using two other larger machines with different nameplate ratings. Moreover, the aforementioned parameters of the larger machines were also experimentally determined using IEEE standard tests. Finally, a comparison of the results obtained employing the conventional and the proposed methodologies was performed, and the proposed methodology has been established to be valid as the results are in close agreement.

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 score0.955
Threshold uncertainty score0.487

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 head0.018
GPT teacher head0.221
Teacher spread0.202 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicElectric Motor Design and AnalysisFrench-language works237,207