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Record W2061899991 · doi:10.1109/pes.2008.4596863

An accurate current transformer model based on preisach theory for the analysis of electromagnetic transients

2008· article· en· W2061899991 on OpenAlexaff
Afshin Rezaei‐Zare, Reza Iravani, Majid Sanaye‐Pasand, Hossein Mohseni, Shahrokh Farhangi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmtpHysteresisTransformerMagnetic hysteresisControl theory (sociology)Transient (computer programming)VoltageElectronic engineeringComputer scienceEngineeringElectric power systemMagnetizationPhysicsPower (physics)Magnetic fieldElectrical engineering

Abstract

fetched live from OpenAlex

Summary form only given. This paper presents a new and accurate current transformer (CT) model for the analysis of electromagnetic transients, based on representation of core magnetization characteristics using the Preisach Theory. Unlike the existing CT models, the proposed model determines shapes of hysteresis minor loops independent of the hysteresis major loop. This results in a higher precision and more flexibility to fit the model hysteresis loops with the actual core material hysteresis loops of the CT. The proposed model has been implemented in the PSCAD/EMTDC software environment. As an application of the model, transient behavior of a CT due to a fault and its subsequent reclosure attempts is investigated and compared with the EMTP Reactor Type-96 based CT model and the IEEE Power System Relaying Committee CT model. Time domain simulation studies show that the transient behavior of the CT is significantly different, if different hysteresis minor loop trajectories but the same hysteresis major loop are adopted. This paper concludes that both hysteresis major and minor loops must be represented to accurately simulate performance of a CT for the reported case studies.

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 score0.678
Threshold uncertainty score0.913

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)0.0010.000

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 head0.035
GPT teacher head0.288
Teacher spread0.253 · 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
Published2008
Admission routes1
Has abstractyes

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