MétaCan
Menu
Back to cohort
Record W2107617316 · doi:10.1109/poweri.2006.1632628

Comparison of performance of various ferroresonance suppressing methods in inductive and capacitive voltage transformers

2006· article· en· W2107617316 on OpenAlexaff
Majid Sanaye‐Pasand, Afshin Rezaei‐Zare, H. Mohseni, Sh. Farhangi, Reza Iravani

Bibliographic record

Venue2006 IEEE Power India Conference · 2006
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFerroresonance in electricity networksTransformerCapacitive sensingElectronic circuitVoltageElectrical engineeringElectronic engineeringCurrent transformerEngineering

Abstract

fetched live from OpenAlex

To avoid or damp out ferroresonance in inductive and capacitive voltage transformers, a few ferroresonance damping methods have been proposed and used in these instruments. In this paper, some of the suppressing circuits are reviewed. Specifications of these circuits and the effects of various parameters on their performances are discussed. Furthermore, using frequency domain analysis, the effects of the suppression circuits on the measured voltage signal and overall characteristics of the voltage transformers in normal operating conditions are investigated. Using time domain simulations, the occurrence of ferroresonance in inductive and capacitive voltage transformers is studied and the effects of the suppressing circuits on the transient performance of these transformers and damping out ferroresonance are investigated and compared to each other

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.032
GPT teacher head0.324
Teacher spread0.292 · 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 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

Citations35
Published2006
Admission routes1
Has abstractyes

Explore more

Same venue2006 IEEE Power India ConferenceSame topicMagnetic Properties and ApplicationsFrench-language works237,207