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Record W1522806499 · doi:10.1109/poweri.2006.1632516

GA-identifier and predictive controller for multi-machine power system

2006· article· en· W1522806499 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

Venue2006 IEEE Power India Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIdentifierControl theory (sociology)Electric power systemController (irrigation)Model predictive controlTransient (computer programming)Computer sciencePower (physics)SIGNAL (programming language)Predictive powerStabilizer (aeronautics)Genetic algorithmControl engineeringEngineeringControl (management)Artificial intelligencePhysicsMachine learning

Abstract

fetched live from OpenAlex

The effectiveness of a genetic algorithm (GA) identifier and a predictive controller to damp multi-mode oscillations in power system is investigated in this paper. A five machine power system is used in the simulation studies and its transient response to disturbances causing multi-mode oscillations is investigated. The GA-identifier tracks the changes in the system conditions. The predictive controller uses pole-shifting numerical optimization routine to determine the optimum control signal. Behavior of the adaptive power system stabilizer (APSS) and its coordination ability with other power system stabilizers in the system are demonstrated.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
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)0.0000.000

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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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