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Record W4400188223 · doi:10.1109/tec.2024.3420897

Wide-Band Constant-Parameter Voltage-Behind- Reactance Model of Squirrel-Cage Induction Machines

2024· article· en· W4400188223 on OpenAlexaff
Sheraz Baig, Seyyedmilad Ebrahimi, Juri Jatskevich, Liwei Wang, Aria Fani

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsReactanceSquirrel-cage rotorVoltageConstant (computer programming)Constant voltageCageInduction motorControl theory (sociology)Electrical engineeringBushingPhysicsEngineeringComputer scienceMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

Variable frequency drives are widely utilized in many commercial and industrial applications. Therein, typically, a three-phase squirrel-cage induction machine (IM) is fed from an inverter through a cable. To design and tune such systems, various models of IMs have been proposed in the literature to study the motor-converter interactions and the low-to-high frequency phenomena. This paper presents a wide-band model for the IMs that is valid in the frequency range from DC to ten MHz. The new model is obtained by incorporating the low-mid frequency voltage-behind-reactance model with the universal high-frequency stator and bearing circuit models. The performance of the proposed model is demonstrated using computer simulations and experimental results obtained from a 7.5 hp induction motor connected to a drive system through a cable. The new model is demonstrated to accurately predict the common mode (CM) and differential mode (DM) impedances, reflected transient over-voltages, CM currents, and bearing voltages, all representing an advantage over the conventional/existing IM models.

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: Empirical · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.988

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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