MétaCan
Menu
Back to cohort
Record W2085767019 · doi:10.1109/tpwrs.2013.2291400

A Trend-Oriented Power System Security Analysis Method Based on Load Profile

2014· article· en· W2085767019 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

VenueIEEE Transactions on Power Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central Universities
KeywordsElectric power systemSecurity analysisComputer scienceReliability engineeringPower flowContingencyMargin (machine learning)Field (mathematics)Power-flow studyPower (physics)Mathematical optimizationEngineeringMathematicsComputer security

Abstract

fetched live from OpenAlex

Conventional power system security analysis based on multiple case studies cannot predict all operational states and hence neither guarantees an adequate security margin nor an economical operation for the power system. Based on the trend analysis method which is used in the field of economics, this paper introduces the concept and a methodology for “trend security analysis” of power systems. This method utilizes the load profile forecast and the contingency occurrence probability and determines the system security trend in the subsequent time window. Based on a recursive algorithm which is developed by utilizing the higher order derivatives of power flow equations, this paper also presents a method for fast determination of the trend variations of the system states and security indices. Three IEEE test systems are used to demonstrate the applications of the proposed concepts, evaluate their performance and verify their accuracy.

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 categoriesMeta-epidemiology (narrow)
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 score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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