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Record W4230942073 · doi:10.1002/9781118849972.ch18

Probabilistic Transient Stability Assessment

2014· other· en· W4230942073 on OpenAlexaff
Wenyuan Li

Bibliographic record

Venuenot available
Typeother
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsProbabilistic logicRandomnessTransient (computer programming)Fault (geology)Stability (learning theory)Monte Carlo methodComputer scienceReliability engineeringEngineeringMathematicsStatisticsArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

This chapter discusses probabilistic transient stability assessment and its actual applications. The probabilistic transient stability assessment needs to evaluate both the probability and consequence of fault events. The probability of fault events depends not only on uncertainties of fault location and type but also on the probability of successful protection action and randomness of fault-clearing time. The chapter discusses the probabilistic modeling and simulation methods. Here, selection of pre-fault system states, fault models, Monte Carlo simulation of fault events, and transient stability simulation are discussed. This is followed by a discussion on the procedure of probabilistic transient stability assessment. Two application examples are also provided.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.134
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1370.003

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 head0.093
GPT teacher head0.393
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2014
Admission routes1
Has abstractyes

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