Determination oftheOptimumRoutine TestandSelf-Checking Intervals inProtective Relaying UsingaReliability Model
Bibliographic record
Abstract
M.;Sidhu, T.S. Author Affiliation: University ofSaskatchewan, Saskatoon, Canada Abstract: Relaying reliability isgenerally separated into thetwodif- ferent aspects ofdependability andsecurity. Thereliability ofaprotec- tion relay canbeimproved bycanying outroutine maintenance orby including built-in monitoring andself-checking facilities during thede- sign stages. AMarkov model isdescribed inthis paper that canbeused to examine these features. Themodel also recognizes common-cause fail- ures, temporary andpermanent faults andtheassociated clearing times, operation ofback-up protection, andrelay mal-trips. Studies havebeen conducted toillustrate theexpected reliability benefits byinclusion of monitoring andself-checking facilities within therelay. Abstract: Aprobabilistic powersystem stabilizer (PSS) design con- sidering multi-operating conditions isproposed inthis paper. Underthe assumption ofnormal distribution, theconventional eigenvalue sensitiv- ityanalysis isextended toprobabilistic environment bydescribing the statistical nature ofelgenvalues asexpectations andvariances. Two probabilistic indices derived fromthesensitivities ofelgenvalue expecta- tions andvariances areintroduced tothePSSsite andparameter selec- tion. Therobustness ofthesystem canbeguaranteed because awide range oftheloadvariation hasbeentaken into account inprobabilistic representation. Theeffectiveness oftheproposed PSSisdemonstrated onathree-machine system byprobabilistic eigenvalue analysis andtran- sient response simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".