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Record W2131707589 · doi:10.1109/tec.2005.847997

Synchronous Generator Model Identification for Control Application Using Volterra Series

2005· article· en· W2131707589 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 Energy Conversion · 2005
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVolterra seriesControl theory (sociology)Permanent magnet synchronous generatorGenerator (circuit theory)Nonlinear systemConvolution (computer science)GeneralizationComputer scienceVoltageElectric power systemSystem identificationSeries (stratigraphy)MathematicsPower (physics)EngineeringData modelingControl (management)Mathematical analysisArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Application of volterra series for identification of a synchronous generator is investigated in this paper. For linear systems, convolution integral can be used to model the input-output data set. For nonlinear systems, however, volterra series serves as a generalization of the convolution integral. To parameterize the volterra kernels, orthogonal functions have been used. The proposed method is first applied on a seventh order nonlinear model of a synchronous generator with saturation effect and then it is tested on a micro-machine system. In this study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous generator. Simulation and experimental results show good accuracy of the identified models.

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 categoriesnone
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 score0.972
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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