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Record W2056265564 · doi:10.1109/icpst.2006.321475

Power System Emergency Control Based on Load Stratification

2006· article· en· W2056265564 on OpenAlexaboutno aff
Jun Wu, Guangyu Tu, Yi Luo, Deshu Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsnot available
Fundersnot available
KeywordsBlackoutElectric power systemLoad SheddingControl theory (sociology)Stratification (seeds)GridComputer sciencePower controlPower gridLoad balancing (electrical power)Load managementPower (physics)Control (management)Control engineeringEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Because of the Aug. 14 blackout in the U.S. and Canada, the subject of maintaining grid security and stabilization after great disturbance increasingly becomes the focus of power companies all around the world. Power system emergency control is the conventional method for maintaining transient stabilization of power grid while disturbance occurs. The emergency control based on load stratification is advanced. Comparing with normal low frequency load/generator shedding,, the theory of load stratification provides the fast creating of control strategy table on stratified load or generator, and the expressions of power balance protection provides rapid calculating on the real value of load/generator shedding. The result of simulation shows that the principle of emergency load control is effective.

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: 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
Metaresearch0.0000.000
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)0.0010.000

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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

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