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Influence of WFSM Parameters, Currents and Control Scheme on Its Dynamic Performance During Active Short Circuit Fault in EV

2021· article· en· W4200494469 on OpenAlexaff
Vamsi Krishna Kurramsetty, Aiswarya Balamurali, Philip Korta, K. Lakshmi Varaha Iyer, Narayan C. Kar

Bibliographic record

Venue2021 24th International Conference on Electrical Machines and Systems (ICEMS) · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsStatorControl theory (sociology)Transient (computer programming)Rotor (electric)Fault (geology)ExciterEngineeringEquivalent circuitComputer scienceVoltageElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Drive initiated active symmetrical short circuit (SSC) of motor phases is used as a protection method in electric vehicle (EV) drives during abnormalities. This paper exclusively investigates the effects of wound field synchronous machine (WFSM) stator and field circuit parameters, currents and control methods on its dynamic performance during a forced SSC fault. This paper employs a novel coupled circuit-based control model that represents the stator subsystem in two-axis model and the field exciter and rotor circuit as physical variables to capture the transient response during freewheeling action towards predicting both steady-state and transient responses of a WFSM to 3-phase SSC faults. Stator non-linear inductances, resistances, stator current angle, field current magnitude, and rotor field winding parameters are included in the model, thus making it comprehensive for performance analysis in WFSMs. Behavior of stator and rotor currents during SSC is analyzed at different pre-fault conditions when the WFSM is controlled using total- and rotor-loss minimization control schemes. Results obtained using the developed model are verified and compared with results obtained from numerical simulations based on finite element model and experimental tests conducted on the test machine.

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: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.850

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 head0.022
GPT teacher head0.259
Teacher spread0.237 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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