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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".