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Record W1974455597 · doi:10.1109/pes.2008.4596867

An accurate hysteresis model for ferroresonance analysis of a transformer

2008· article· en· W1974455597 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
KeywordsFerroresonance in electricity networksTransformerEmtpMagnetic hysteresisHysteresisControl theory (sociology)VoltageMagnetizationMaterials scienceComputer scienceEngineeringPhysicsMagnetic fieldElectrical engineeringThermodynamicsCondensed matter physicsElectric power system

Abstract

fetched live from OpenAlex

Summary form only given. This paper introduces an accurate transformer core model, using the Preisach Theory, to represent the core magnetization characteristic. This modeling approach provides the required precision to match major and minor hysteresis loops of the model with those of the actual transformer core material. Using the proposed model, the ferroresonance phenomenon of a Voltage Transformer (VT) is simulated and compared to the corresponding experimental results. In addition, the simulated ferroresonance behavior of the VT based on (i) the EMTP-RV Hysteric Reactor model, (ii) the EMTP Reactor Type-96 model, and (iii) a single-valued polynomial as magnetization characteristic, is also deduced and compared with the proposed model and the experimental results.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.613

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 head0.069
GPT teacher head0.298
Teacher spread0.230 · 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 designBench or experimental
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

Citations6
Published2008
Admission routes1
Has abstractyes

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