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Record W2142727519 · doi:10.1109/tpwrs.2006.882454

Multivariable Adaptive Control of Synchronous Machines in a Multimachine Power System

2006· article· en· W2142727519 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 Transactions on Power Systems · 2006
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultivariable calculusControl theory (sociology)Electric power systemController (irrigation)Subspace topologyControl engineeringAdaptive controlStability (learning theory)Computer scienceModel predictive controlProjection (relational algebra)EngineeringPower (physics)Control (management)Artificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

A multivariable self-tuning adaptive control scheme used to enhance power system stability in a multimachine environment is presented. The controller is implemented locally for individual generators with supplementary stabilizing signals through the AVR and governor. A discrete multivariable state space model is developed to represent the generator. The recursive subspace identification method based on the projection approximation subspace tracking approach is employed to update generator model parameters online. A generalized predictive control strategy with constraints on the control signals is used. Simulations studies carried out on a five-machine power system without infinite bus show that the proposed multivariable adaptive controller is effective in damping both local and interarea mode oscillations under small as well as large disturbances. The self-coordinating ability of the adaptive controller with the existing conventional controllers is also demonstrated.

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 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 score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.188
Teacher spread0.184 · 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