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Record W1972395051 · doi:10.1049/iet-gtd.2013.0493

Detecting synchrophasors computed over fault/switching transients

2014· article· en· W1972395051 on OpenAlexaff
Sarasij Das, T.S. Sidhu

Bibliographic record

VenueIET Generation Transmission & Distribution · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsOntario Tech UniversityWestern University
Fundersnot available
KeywordsFault (geology)Computer scienceControl theory (sociology)Artificial intelligenceGeologySeismology

Abstract

fetched live from OpenAlex

Faults and switching operations often cause step changes in voltage and current waveforms. A fault/switching transient synchrophasor is computed over a window of pre‐ and post‐fault/switching samples. Many synchrophasor applications are not designed to use these transient synchrophasors. So, there is a need to detect fault/switching transient synchrophasors. A robust algorithm is proposed to detect synchrophasors computed over switching/fault transients. The proposed algorithm runs in phasor measurement unit. The proposed algorithm performs satisfactorily in the presence of noises, harmonics, off nominal frequencies, decaying DC and missing/bad samples. The proposed algorithm can detect fault/switching transient synchrophasors caused by smaller or larger disturbances. The proposed algorithm is simple, non‐iterative and mainly needs additions. Possible applications of the proposed algorithm are also discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.894

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations5
Published2014
Admission routes1
Has abstractyes

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