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Record W2547975447 · doi:10.1109/ccece.2016.7726855

A parametric dynamic phasor model of line-commutated rectifier systems

2016· article· en· W2547975447 on OpenAlexaff
Yingwei Huang, Seyyedmilad Ebrahimi, Navid Amiri, Hamid Atighechi, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsBC Hydro (Canada)University of British Columbia
Fundersnot available
KeywordsPhasorRectifier (neural networks)Parametric statisticsComputer scienceControl theory (sociology)Parametric modelLine (geometry)Power (physics)Set (abstract data type)Electric power systemMathematicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Recently, the generalized averaging method (GAM) has received significant attention to effectively model and simulate dynamics of ac-dc power systems using dynamic phasors (DPs). The conventional analytical DP (ADP) models of line-commutated rectifiers (LCRs) have been derived based on switch functions, which are only valid for certain operating mode. In this paper, a new parametric DP (PDP) model of LCRs is presented, which relates the ac-dc subsystems through a set of parametric algebraic functions that account for various loading conditions. Simulation results validate the advantageous accuracy of the proposed PDP model of LCRs, and demonstrate its superior numerical efficiency over the time-domain detailed model and the prior ADP model.

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: none
Teacher disagreement score0.667
Threshold uncertainty score0.273

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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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
Published2016
Admission routes1
Has abstractyes

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