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Record W2553609148 · doi:10.1109/pesgm.2016.7741967

Improved constant parameter VBR model of permanent magnet synchronous machine using resonant auxiliary rotor winding

2016· article· en· W2553609148 on OpenAlexaff
Navid Amiri, Seyyedmilad Ebrahimi, Yingwei Huang, Juri Jatskevich, H.W. Dommel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReactanceConvertersRotor (electric)Control theory (sociology)Variable bitrateInductanceComputer scienceMagnetConstant (computer programming)Power (physics)Wind powerElectronic engineeringVoltageEngineeringElectrical engineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Wind power generation with permanent magnet synchronous machines (PMs) has been made possible using power electronic converters. With the increasing use of such systems, the numerically efficient modeling and simulation are of critical importance. As an alternative to traditional qd models, the coupled-circuit phase-domain (CCPD) and voltage-behind-reactance (VBR) models have been recently proposed to achieve advantageous numerical properties. Constant parameter VBR (CPVBR) models has also been presented to avoid having the variable inductance matrix that is present in CCPD and VBR models in case of salient rotor machines. This paper improves the CPVBR model using the auxiliary rotor winding to achieve better accuracy when the machine in connected to power electronic converters. The new model is shown to possess very good accuracy and advantages over alternative existing 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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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