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Record W4380318884 · doi:10.32920/23502327

RTDS Based Testing of Bus Differential Protective Relay Performance in Presence of CT Saturation

2023· preprint· en· W4380318884 on OpenAlexaff
Ishwarjot Anand

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsBusbarRelayDifferential protectionProtective relayCurrent transformerTrippingSaturation (graph theory)EngineeringElectric power systemElectrical impedanceElectronic engineeringElectrical engineeringCircuit breakerEmbedded systemTransformerVoltagePower (physics)PhysicsMathematics

Abstract

fetched live from OpenAlex

A power system busbar can experience very high amounts of currents during short circuit faults, resulting in significant and often irreparable damage. Therefore, bus protection systems need to have fast, reliable, and secure operation. Amongst the various bus protection schemes, the low-impedance bus differential schemes have become increasingly popular due to their less challenging application requirements; however, they can be severely impacted by current transformer (CT) saturation during higher fault currents. The modern numerical relays try to mitigate the CT saturation problem by processing the initial saturation free current measurements and using stabilization techniques. In this project, the low impedance bus differential function of a modern bus protection relay is tested under severe CT saturation, such that the saturation free time is minimized to lower than the advertised minimum saturation free time. The testing is based on hardware-in-the-loop (HIL) setup that utilizes the physical relay device and the Real Time Digital Simulator (RTDS).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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