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Record W3143819401 · doi:10.1109/pes.2004.1373138

Large power system stability enhancement using wide-area signals based hierarchical controller

2004· article· en· W3143819401 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 Power Engineering Society General Meeting, 2004. · 2004
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)GovernorController (irrigation)Electric power systemStability (learning theory)Control engineeringComputer sciencePower (physics)Multivariable calculusEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

A two-level hierarchical structure is proposed to improve power systems stability under severe contingencies. The solution consists of a local controller for each generator at the first level helped by a multivariable central one at the secondary level. The secondary level controller uses remote signals from all the generators to synthesize two outputs, which decouple the subsystems dynamics hence maximizing the local controllers' performances. The first level controllers use local signals exclusively to dampen local oscillations. A systematic procedure for the design of the wide-area signals based controller is given and is based on a reformulation of the multimachine power system model into a suitable and closed form. The hierarchical structure is used on a realistic power system and simulation results show that the system stability is considerably improved. A power system, unstable under the classical controllers (AVR-PSS/GOVERNOR), is rendered stable when we combine the central and local controllers' actions.

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 score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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