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Record W2781854412 · doi:10.1002/9781118755471.sgd090

Dynamic Security Assessment

2016· other· en· W2781854412 on OpenAlexaff
Lei Wang, Xi Lin, Fred Howell, K. Morison

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsBC Hydro (Canada)Powertech Labs (Canada)
Fundersnot available
KeywordsSoftware deploymentComputer scienceSecurity controlsComputer securityControl (management)Systems engineeringSoftware engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This chapter covers the subject of dynamic security assessment (DSA) of power systems. Development of advanced DSA technologies is considered as part of the trend in building smart control centers. After a short discussion on the concept of power system security analysis, three basic elements of DSA are described, namely, security criteria and contingencies to be applied, models to be used, and analysis methods to perform the studies. This is followed by DSA applications using the traditional off‐line approach and more advantageous online approach. Further details for the deployment of the online DSA systems are provided to illustrate the applications of this innovative technology.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.205
Threshold uncertainty score0.959

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)0.0420.001

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 head0.003
GPT teacher head0.242
Teacher spread0.239 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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