MétaCan
Menu
Back to cohort
Record W2027169845 · doi:10.1504/ijmic.2009.023530

Adaptive and intelligent control applications to power system stabiliser

2009· article· en· W2027169845 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

VenueInternational Journal of Modelling Identification and Control · 2009
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRobustness (evolution)Control engineeringStabiliserComputer scienceControl theory (sociology)Adaptive controlElectric power systemController (irrigation)Robust controlControl systemEngineeringPower (physics)Control (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Adaptive control can be described as the changing of controller parameters online based on the changes in system operating conditions. Adaptive controllers based on analytical techniques can provide excellent performance and improve the dynamic performance of the plant by allowing the parameters of the controller to adjust as the operating conditions change. Proper care needs to be taken to make them robust, especially under large disturbances. Controller robustness can be improved by employing artificial intelligence (AI) techniques. It is possible to implement either the entire algorithm using AI techniques or by integrating analytical and AI techniques such that some functions are performed using analytical approach and the rest using AI techniques. Successful implementation of purely analytical, purely AI and integrated approaches is illustrated by application to a power system stabiliser to improve damping and stability of an electric generating unit.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.437

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 head0.231
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