MétaCan
Menu
Back to cohort
Record W3142357520 · doi:10.1109/mper.2002.4312137

Impact of Six Phase Transmission Line Faults on Turbine-Generator Shaft Torsional Torques

2002· article· en· W3142357520 on OpenAlexaff
S.O. Faried, S. Upadhyay, S. Al-Senaidi

Bibliographic record

VenueIEEE Power Engineering Review · 2002
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTorqueTurbineTransmission lineSteam turbineFault (geology)Generator (circuit theory)EngineeringContext (archaeology)Control theory (sociology)Line (geometry)Power (physics)Structural engineeringComputer scienceElectrical engineeringPhysicsMechanical engineeringMathematicsGeologyControl (management)

Abstract

fetched live from OpenAlex

This paper explores the impact of six-phase transmission line faults and their subsequent fault clearing and reclosing on the torsional torques induced in turbine-generator shafts. In this context, investigations have been conducted on a large turbine-generator unit connected to a sample power system. The paper presents also a comparison between the torsional stresses induced in the turbine-generator shafts during these events with their corresponding disturbances of a three-phase transmission system. The results of these investigations are presented in the form of typical time responses as well as parametric studies. The EMTP is used for the simulation studies. The studies conclude that six-phase transmission line faults generally induce torsional torques in the turbine-generator shaft whose magnitudes are relatively higher than those induced due to transmission line faults in a three-phase system. This conclusion is true for both sustained faults and faults followed by clearing and high-speed reclosing operations.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.016
GPT teacher head0.316
Teacher spread0.300 · 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 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

Citations1
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Power Engineering ReviewSame topicMachine Fault Diagnosis TechniquesFrench-language works237,207