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Record W2948555760 · doi:10.1109/tpwrs.2019.2920782

A Self-Adjusting Adaptive AVR-LFC Scheme for Synchronous Generators

2019· article· en· W2948555760 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 · 2019
Typearticle
Languageen
FieldEngineering
TopicFrequency Control in Power Systems
Canadian institutionsUniversity of OttawaUniversité de Moncton
Fundersnot available
KeywordsRobustness (evolution)Control theory (sociology)Automatic Generation ControlElectric power systemNonlinear systemComputer scienceVoltage regulatorHeuristicsPID controllerVoltageControl engineeringAdaptive controlMathematical optimizationPower (physics)EngineeringControl (management)MathematicsTemperature control

Abstract

fetched live from OpenAlex

An efficient self-adjusting flexible metaheuristic is introduced to develop automatic voltage and load frequency regulation schemes for multi-area power system. It employs non-linear threshold accepting algorithm to find the best combinations of PID control gains using an innovative nonlinear search function. This approach outperformed well established heuristics and other conventional internal model control approaches in terms of the dynamic response, robustness in uncertain dynamical environments, and quality of the solutions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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