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Record W2897827895 · doi:10.1109/sege.2018.8499446

Simulation of Line-Commutated Rectifier Systems Using Fixed Time-Step without Zero-Crossing Events

2018· article· en· W2897827895 on OpenAlexaff
Seyyedmilad Ebrahimi, Navid Amiri, Juri Jatskevich, Liwei Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsEmtpRectifier (neural networks)Computer scienceInterpolation (computer graphics)Zero crossingHarmonicsConvertersTransient (computer programming)Parametric statisticsLine (geometry)Power (physics)Control theory (sociology)Electric power systemElectronic engineeringAlgorithmVoltageElectrical engineeringEngineeringMathematicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Line-commutated rectifiers (LCRs) are widely used in many industrial applications and electronic loads. For analysis and simulation of power systems that include many switching converters, numerically efficient and accurate models of rectifiers are needed. The detailed switching models of LCRs in traditional electromagnetic transient (EMT) simulation programs (either state-variable-based or EMTP-type) require special handling of switching events (i.e., interpolation and/or use of small time-steps for zero crossing detection), which results in increased computational complexity. This paper presents the recently developed generalized parametric average-value model (GPAVM) of LCRs that is capable of predicting the ac harmonics of interest with good accuracy while using fairly large fixed time-steps without the need for handling the zero-crossing events. This feature represents an advantage over the established methods and may be utilized for more efficient simulation of power systems with many rectifier loads.

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 score0.397
Threshold uncertainty score0.693

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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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