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Record W2171630433 · doi:10.1109/tpwrd.2009.2028818

Analysis and Suppression of the Coupling Capacitor Voltage Transformer Ferroresonance Phenomenon

2009· article· en· W2171630433 on OpenAlexaff
Firouz Badrkhani Ajaei, Majid Sanaye‐Pasand, Afshin Rezaei‐Zare, Reza Iravani

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2009
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFerroresonance in electricity networksTransformerOvervoltageSnubberControl theory (sociology)CapacitorEngineeringElectrical engineeringVoltageEddy currentElectronic engineeringComputer science

Abstract

fetched live from OpenAlex

This paper provides a detailed core model for the step-down transformer (SDT) of the coupling capacitor voltage transformer (CCVT) to investigate the CCVT transients and ferroresonance behavior. The core model represents hysteresis symmetric and asymmetric minor loops, remnant flux, and the eddy current effects. Based on the developed model, the impacts of passive and active ferroresonance suppression circuits (PFSC and AFSC) and overvoltage protection devices (OPDs) for fast suppression of the CCVT ferroresonance phenomenon are also studied. This paper also presents a generalized methodology to select the AFSC parameters. The simulation results, in the PSCAD/EMTDC environment, indicate that the hysteresis and eddy current effects of the SDT core significantly influence the CCVT ferroresonance behavior. The study results also show that: 1) in both the presence and the absence of arresters and spark gaps, the AFSC can mitigate the phenomenon faster than the PFSC, i.e., in about two cycles and 2) the output fidelity of the AFSC-based CCVT is less dependent on the burden as compared with that of the PFSC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2009
Admission routes1
Has abstractyes

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