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Record W1818650917 · doi:10.1109/pesc.2003.1218325

Modeling simulation and performance analysis of switched reluctance motor operating with optimum value of fixed turn-on and turn-off switching angles

2004· article· en· W1818650917 on OpenAlexafffund
H. Akhter, V.K. Sharma, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsÉcole de Technologie Supérieure
FundersTehran University of Medical Sciences and Health ServicesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTurn (biochemistry)Switched reluctance motorValue (mathematics)Computer scienceControl theory (sociology)Reluctance motorSimulationTorquePhysicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

The value of turn-on and turn off angles are important factors in developing positive or negative, high or low electromagnetic torque in switched reluctance motor (SRM) and lead to stable or unstable operation of the drive. This paper presents the performance of a 4 kW 8/6-pole configuration SRM drive through modeling and simulation for optimum and fixed value of switching-on and -off angles. Normally, these switching angles depend upon the motor speed and are varied with acceleration. In this paper, the analysis is conducted to arrive at an optimum pair of switching angle for full-load starting and stable operation of the drive. The simulated performance of SRM drive system is presented to analyze the effect of switching angles on transient and steady state performance of the drive in terms of speed, current and torque response. The merit of fixed switching angle control schemes is highlighted.

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: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.568

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)0.0000.000

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations7
Published2004
Admission routes2
Has abstractyes

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