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Record W1992458060 · doi:10.1109/pesmg.2013.6672636

Generalized state-space saturable induction machine model using a voltage-behind-reactance formulation

2013· article· en· W1992458060 on OpenAlexaff
Francis Therrien, Mehrdad Chapariha, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReactanceControl theory (sociology)InterfacingComputer scienceElectromagnetic coilRotor (electric)Transient (computer programming)Variable bitrateDiscretizationVoltageControl engineeringMathematicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Modeling of induction machines (IMs) for power systems transient studies has been receiving significant attention in the literature, where a continuous effort is made to improve the accuracy and efficiency of common general-purpose models. Among recently proposed methods, a so-called voltage-behind-reactance (VBR) formulation has been shown to improve the interfacing of machine models with the external network. Depending on the rotor type and the required model fidelity, the rotor may be represented using one or two rotor equivalent windings. In this paper, we propose a new algebraically exact generalized IM model using the VBR formulation. The proposed full-order model incorporates two rotor windings and main flux saturation, whereas a simpler model with only one rotor winding can also be easily obtained. The validity of the new model is first verified by comparing it to a traditional (reference) model. The improved accuracy and computational efficiency of the proposed VBR model is then demonstrated when the IM is interfaced to an external inductive network.

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)0.000
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: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.649

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 communication0.0000.001
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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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