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Record W4256385187 · doi:10.1002/047134608x.w6127.pub2

Excitation Control in Power Systems

2017· other· en· W4256385187 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

VenueWiley Encyclopedia of Electrical and Electronics Engineering · 2017
Typeother
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsControl theory (sociology)Electric power systemExcitationGenerator (circuit theory)Nonlinear systemSIGNAL (programming language)Power (physics)Stability (learning theory)Computer sciencePermanent magnet synchronous generatorControl engineeringControl systemVoltageControl (management)EngineeringElectrical engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Excitation system, an integral part of the synchronous generator, performs control and protection functions that include control of voltage and reactive power. Power system stability can also be enhanced by supplying additional signal through the excitation system. This function is performed by the power system stabilizer ( PSS ) that generates a control signal introduced as a supplementary input into the excitation system. Due to the nonlinear characteristics, wide operating conditions, and unpredictability of perturbations in a power system, the conventional fixed parameter PSS generally cannot maintain the same quality of performance under all operating conditions. To improve power system performance and stability, various approaches have been proposed. An overview of the development and successful implementation of various approaches, including adaptive PSSs based on analytical and artificial intelligence techniques, is provided.

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 categoriesnone
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 score0.975
Threshold uncertainty score0.963

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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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