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Record W4401630802 · doi:10.1016/j.ifacol.2024.07.301

Resilience Metrics in Power & Control Systems: Comparative Analysis

2024· article· en· W4401630802 on OpenAlexaff
M. Ghanbari, Jin Jiang

Bibliographic record

VenueIFAC-PapersOnLine · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsWestern University
Fundersnot available
KeywordsResilience (materials science)Power (physics)Environmental scienceControl (management)Computer scienceRisk analysis (engineering)Reliability engineeringEngineeringArtificial intelligenceBusinessMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Amid the increasing frequency of extreme events, traditional approaches in control and power systems are proving ineffective. There’s an essential need for a new perspective to manage the emerging risk landscape and to prioritize the recovery process. Resilience, the ability to cover events across a spectrum from common to catastrophic, extending from pre-event to post-event periods, offers a potential solution. Despite significant research, a universal standard for resilience metrics is lacking. This paper delves into this issue, studying the main existing metrics to pinpoint gaps and suggest future directions for standardized control and power systems resilience assessment.

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: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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