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Record W2146534582 · doi:10.1109/tdc.1996.545946

Mitigating potential transformer ferroresonance in a 230 kV converter station

2002· article· en· W2146534582 on OpenAlexaffabout
David Jacobson, D.R. Swatek, R.W. Mazur

Bibliographic record

VenueProceedings of 1996 Transmission and Distribution Conference and Exposition · 2002
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsManitoba Hydro
Fundersnot available
KeywordsFerroresonance in electricity networksCircuit breakerResistorTransformerElectrical engineeringInductanceEngineeringCapacitanceNonlinear systemVoltagePhysics

Abstract

fetched live from OpenAlex

A wound potential transformer failed catastrophically on the Manitoba Hydro system. The failure was attributed to excessive current flow in the primary winding due to a sustained ferroresonance between the circuit breaker grading capacitance and nonlinear magnetizing inductance of the potential transformer. In order to prevent future occurrences, switching guidelines have been revised and permanently connected damping resistors were installed. Modern nonlinear techniques of analysis are evaluated for their suitability of defining safe and unsafe zones of operation. A new two-dimensional bifurcation diagram is proposed as a new visualization tool.

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)0.001
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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

Citations35
Published2002
Admission routes2
Has abstractyes

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