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Record W2009613302 · doi:10.1109/pesgm.2012.6345135

A VSC-HVDC model with reduced computational intensity

2012· article· en· W2009613302 on OpenAlexaff
Mohamed M. Zakaria Moustafa, Shaahin Filizadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsVoltage sourceConvertersTransient (computer programming)VoltageComputer scienceElectronic engineeringHigh-voltage direct currentPower (physics)Current sourceEmtpControl theory (sociology)Electric power systemEngineeringElectrical engineeringDirect currentPhysics

Abstract

fetched live from OpenAlex

The paper presents a simplified voltage-source converter (VSC) model to reduce the computational intensity of simulating a power system with embedded converters. This simplified model is based on the concept of dynamic average-value modeling and provides the ability to generate either the full spectrum or the fundamental-frequency component of the VSC voltage. The model is validated against a detailed model of a voltage-source converter based high voltage direct current (VSC-HVDC) circuit and shows accurate matching during steady state and transient operation. Simulation and validation results are obtained using an electromagnetic transient simulation program (PSCAD/EMTDC). A significant reduction in terms of central processing unit (CPU) time consumption is also achieved with the proposed 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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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