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Record W2163211273 · doi:10.1109/icit.2004.1490789

An adaptive fuzzy controller gain scheduling for power system load-frequency control

2005· article· en· W2163211273 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFrequency Control in Power Systems
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsControl theory (sociology)Gain schedulingAutomatic frequency controlComputer scienceController (irrigation)Electric power systemFuzzy control systemFuzzy logicAdaptive controlOpen-loop controllerFrequency scalingAdaptive neuro fuzzy inference systemControl engineeringPower (physics)EngineeringVoltageControl (management)

Abstract

fetched live from OpenAlex

In this paper, an adaptive fuzzy controller gain scheduling scheme for power system load-frequency control is designed to damp the frequency oscillations and to track its error to zero at steady state. A Sugeno type inference system is used in the proposed controller to adapt the scaling gains of a single fuzzy controller through a classical on-line monitoring of the most sensitive parameters of the system. The proposed controller avoids excessive patterns and training time compared to neural network based adaptive schemes. A typical single-area non reheat power system is considered. Simulation results indicate that the proposed controller is insensitive to parameter changes in a wide range of operating condition, and to the generation rate constraints. Furthermore, it is simple to implement.

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)0.001
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 score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Quick stats

Citations43
Published2005
Admission routes1
Has abstractyes

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