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Record W2161656712 · doi:10.1109/pes.2007.386201

A New Stabilizer and Design Algorithm to Minimize the Excitation of Undesirable Oscillations

2007· article· en· W2161656712 on OpenAlex
N. Kshatriya, U.D. Annakkage, A.M. Gole

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 · 2007
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStabilizer (aeronautics)Control theory (sociology)Eigenvalues and eigenvectorsOscillation (cell signaling)Low-frequency oscillationElectric power systemController (irrigation)Power (physics)Compensation (psychology)State spaceExcitationPhase compensationMode (computer interface)Computer scienceMathematicsEngineeringPhysicsElectronic engineeringControl (management)

Abstract

fetched live from OpenAlex

A new state-space type stabilizer and its design algorithm to minimize low frequency oscillation is proposed. The proposed power system stabilizer (PSS) design algorithm not only damps the oscillations by moving the eigenvalues to desired left hand plane locations but additionally reduces the excitation of the mode itself through optimization of the left eigenvector of the poorly damped electro-mechanical mode. The stabilizer is implemented as a state-space (SS) type controller which is optimized using a constrained optimization procedure. Two possible inputs, speed and electrical power are considered as candidate inputs to the stabilizers. The state-space design is also compared with one based on traditional lead-lag compensation. The SS type stabilizer with electrical power input is shown to perform better compared to any other type of PSS by minimizing low frequency oscillations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.397
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.223
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