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Record W1598130868 · doi:10.1109/pes.2003.1267428

Adaptive and artificial intelligence based PSS

2004· article· en· W1598130868 on OpenAlexaff
O.P. Malik

Bibliographic record

Venue2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491) · 2004
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsControl theory (sociology)Operating pointTransfer functionLinear systemElectric power systemController (irrigation)Computer scienceControl engineeringPermanent magnet synchronous generatorAdaptive controlGenerator (circuit theory)Power (physics)EngineeringMathematicsArtificial intelligenceVoltageControl (management)Electronic engineering

Abstract

fetched live from OpenAlex

The supplementary stabilizing signal input to the excitation system of a synchronous generator enhances system damping by producing a torque in phase with the speed of the generator. The most common PSS (CPSS) is based on the use of a linear transfer function designed by applying the linear control theory to the system model linearized at a pre-assigned operating point. Power systems are non-linear and operate over a wide range. Due to the non-linear characteristics, wide operating conditions and unpredictability of perturbations in a power system, the CPSS, a linear controller, generally cannot maintain the same quality of performance under all conditions of operation. To further improve the performance and stability of the power system, various other approaches using the linear quadratic optimal control, H-infinity, variable structure, rule base, and artificial intelligence (AI) techniques have been proposed in the literature to design a fixed parameter PSS. One common feature of all fixed parameter controllers is that the design is done off-line.

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 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 score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.211
Teacher spread0.198 · 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 teacher head, not a consensus.

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

Citations4
Published2004
Admission routes1
Has abstractyes

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