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Record W2547519970 · doi:10.1109/epc.2008.4763347

Bulk power system restoration interdependency risk modeling

2008· article· en· W2547519970 on OpenAlexaff
Nathan Ozog, Eric Desjardins, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of British ColumbiaBC Hydro (Canada)
Fundersnot available
KeywordsBlackoutInterdependenceElectric power systemComputer scienceReliability engineeringCascading failureTransmission (telecommunications)Power (physics)Electric powerMaintenance engineeringElectric power transmissionRisk analysis (engineering)TelecommunicationsSystems engineeringEngineeringElectrical engineeringBusiness

Abstract

fetched live from OpenAlex

The interdependencies that exist within and between infrastructures can cause unexpected system properties to emerge when their components fail due to large disruptions. As witnessed following emergencies such as the North American 2003 Blackout, the complexities of these interdependencies make it very difficult to effectively recover infrastructure because of the challenges they create in prioritizing the most critical components. To study dependencies in electrical bulk power systems, an interdependency model of the British Columbia Transmission Corporation bulk power network and its communications system was developed, along with a post-blackout restoration procedure. Using these, simulations of a post-blackout recovery were carried out to study the level of risk that communications outages may pose to the electrical networkpsilas recovery. These simulations revealed a correlation between restoration time and the number of communication points lost. This research also demonstrated there is value in combining the results of such simulations with risk evaluation tools.

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)0.000
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 score0.541
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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 teacher head, 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

Citations8
Published2008
Admission routes1
Has abstractyes

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