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Record W2343908830 · doi:10.1049/iet-gtd.2015.0718

Real‐time digital multi‐function protection system on reconfigurable hardware

2016· article· en· W2343908830 on OpenAlexafffund
Yifan Wang, Venkata Dinavahi

Bibliographic record

VenueIET Generation Transmission & Distribution · 2016
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDigital protective relayField-programmable gate arrayEmulationRelayComputer scienceHardware emulationVirtexEmbedded systemComputer hardwareDigital signal processingProtective relaySignal processingPower (physics)

Abstract

fetched live from OpenAlex

This study proposes a multi‐function power system protective relay hardware design built with various functional hardware processing cores on the field programmable gate array (FPGA). This practical and systematic method lends itself to a paralleled and pipelined hardware emulation of individual signal processing and protection components. Detailed emulation designs are presented for the following protective relays: distance, directional overcurrent, voltage, and frequency protection. The necessary signal processing functions required to operate these relays are also emulated, allowing the protection system to be stand‐alone and fed with instantaneous fault data. Real‐time experimental results are presented to verify the functions of the target hardware relay on the Xilinx ® Virtex‐7 FPGA. Case studies provide the efficacy of the multi‐function relay design in terms of accuracy, latency, and resource consumption.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.199
Teacher spread0.184 · 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

Citations18
Published2016
Admission routes2
Has abstractyes

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