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Record W2552231072 · doi:10.1109/pesgm.2016.7741232

Prototype of a negative sequence turn-to-turn fault detection scheme for transformers

2016· article· en· W2552231072 on OpenAlexaff
Daniel Zacharias, Ramakrishna Gokaraju

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRelayDelta-wye transformerTransformerCurrent transformerElectromagnetic coilRotary variable differential transformerDistribution transformerAutotransformerIsolation transformerLinear variable differential transformerProtective relayControl theory (sociology)Energy efficient transformerElectrical engineeringEngineeringVoltageComputer scienceElectronic engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Summary form only given. Digital relays are capable of computing the negative sequence current on both primary and secondary sides of the transformer along with the phase difference between these two negative sequence currents. By using both phase and magnitude information, negative sequence current could be used to detect minor turn-to-turn faults involving 3% of the transformer's windings or more. Turn-to-turn faults may still occur even if no current is flowing on one side of the transformer, such as during energization. With no current flowing in the secondary windings of the transformer, negative sequence current based algorithms become insensitive. This paper introduces a relay prototype, using both negative sequence current and negative sequence voltage, which retains its sensitivity during energization. The relay's performance for several commonly encountered system scenarios such as over-excitation, current transformer saturation, non-zero fault resistance, transformer energization, and external faults were also examined. The experimental results presented in this paper indicate that the algorithm proposed in this paper is faster and more sensitive than restrained current differential protection and capable of detecting turn-to-turn faults occurring during transformer energization.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.385

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)0.0000.000

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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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