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Record W2109781425 · doi:10.1109/tpwrs.2010.2046504

A Power Oscillation Damping Control Scheme Based on Bang-Bang Modulation of FACTS Signals

2010· article· en· W2109781425 on OpenAlexaff
Huy Nguyen‐Duc, Louis‐A. Dessaint, Aimé Francis Okou, Innocent Kamwa

Bibliographic record

VenueIEEE Transactions on Power Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsHydro-QuébecRoyal Military College of CanadaÉcole de Technologie Supérieure
Fundersnot available
KeywordsControl theory (sociology)Robustness (evolution)Electric power systemScheme (mathematics)Oscillation (cell signaling)Power controlRobust controlControl systemBang–bang controlModulation (music)EngineeringPower (physics)Computer scienceControl (management)PhysicsOptimal controlMathematics

Abstract

fetched live from OpenAlex

In this paper, we develop a new damping control scheme for FACTS devices, using bang-bang modulation of FACTS signals. The scheme is used to attenuate quickly the system's most dominant mode which is identified using online Prony analysis. An analysis framework to evaluate the robustness of the control schemes to changes in operating conditions and to time-delay is also proposed. Simulation results on various test systems show that following large disturbances, the proposed control scheme is very effective to mitigate the power system critical modes of oscillation. Furthermore, the problem of control interactions is completely avoided with the proposed control scheme.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.215
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2010
Admission routes1
Has abstractyes

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