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Record W2937246245 · doi:10.1049/iet-gtd.2019.0133

Enhanced probabilistic approach for substation reliability assessment

2019· article· en· W2937246245 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Generation Transmission & Distribution · 2019
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsManitoba Hydro
Fundersnot available
KeywordsReliability engineeringProbabilistic logicReliability (semiconductor)ContingencyPower flowElectric power systemComputer scienceProbabilistic methodEngineeringPower (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

A new enhanced probabilistic approach for substation reliability assessment is presented. The proposed technique evaluates substation reliability using a combinative approach that considers not only the impact of substation equipment failures but also the effect of the system conditions in which the substation resides. The approach uses the analytical contingency enumeration technique and uses a commercial program to perform power flow analysis in order to assure the accuracy of the reliability evaluations. The application of the proposed approach is illustrated by performing probabilistic reliability assessment on an actual utility substation. The technique presented will enable more realistic assessment of the reliability performance of substations and/or switching stations using probabilistic approaches.

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.

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)0.001
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 score0.839
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.236
Teacher spread0.224 · 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