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Record W4313413131 · doi:10.1109/tia.2022.3233558

Testing Ground Fault Protection of Generating Units With Frequency-Selective Grounding

2023· article· en· W4313413131 on OpenAlexaff
S. A. Saleh, Razzaqul Ahshan, Marcelo E. Valdes, D. Jewett, Claudio S. Mardegan, Julian Meng, Sergio Panetta

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGroundResidual-current deviceElectrical impedanceFault (geology)EngineeringEarthing systemHigh impedanceElectrical engineeringGenerator (circuit theory)Electronic engineeringPhysicsPower (physics)Circuit breaker

Abstract

fetched live from OpenAlex

This paper experimentally evaluates responses of ground fault protection for synchronous generators, which are grounded using frequency-selective grounding. Frequency-selective grounding is designed to provide a low ground impedance path (similar to solid grounding) for high frequency zero-sequence current components. This grounding system also provides an impedance similar to a low-resistance grounding for low frequency zero-sequence current components. The frequency-dependent equivalent impedance of frequency-selective grounding can affect ground potential and current, which are typically used as inputs for ground fault protective relays. Effects of frequency-selective grounding on ground fault protection are experimentally evaluated using a 7.5 kVA synchronous generator. Experimental tests are conducted for different implementations of ground fault protection, when responding to fault and non-fault events. Evaluation results detailed in this paper demonstrate the negligible effects of frequency-selective grounding on responses of different ground fault protective relays.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.253
Teacher spread0.208 · 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

Citations30
Published2023
Admission routes1
Has abstractyes

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