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Record W2952194353 · doi:10.1109/icps.2019.8733333

Frequency-Selective Grounding for 3ϕ Power Transformers

2019· article· en· W2952194353 on OpenAlexaff
S. A. Saleh, X. F. St. Onge, Christian M. Richard, E. Ozkop, Sergio Panetta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGroundElectrical engineeringTransformerHarmonicsEngineeringElectrical impedanceElectromagnetic coilResistive touchscreenVoltageElectronic engineering

Abstract

fetched live from OpenAlex

Three phase (3φ) power transformers can have their windings configured to create local neutral points that can be grounded. The grounding of a 3φ power transformer is designed to limit ground fault currents and maintain low ground potentials. These constraints can be translated into a resistive grounding that is usually designed based on the system ratings and parameters. The resistive grounding, however, can impact the flow of the harmonic components present in the exciting currents. As a result, undesired harmonic components are induced in primary and secondary voltages. This paper presents the design and performance of a frequency-selective grounding that can meet the constraints for grounding a 3φ power transformer. The developed grounding is designed to provide a resistive path for low frequency currents (faults), while providing a low impedance path for high frequency currents (harmonics). The frequency-selective grounding is experimentally tested for a 3φ power transformer with different primary and secondary winding configurations, different fault types, and source grounding. Test results show that the developed grounding can reduce the ground potential, harmonic distortion in primary and secondary voltages, and ground fault currents with a minimum interference with ground fault protective devices.

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 score0.634
Threshold uncertainty score0.444

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 head0.005
GPT teacher head0.203
Teacher spread0.199 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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