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Record W2740431598 · doi:10.1109/jpets.2017.2718505

Comprehensive Electromagnetic Transient Simulation of AC/DC Grid With Multiple Converter Topologies and Hybrid Modeling Schemes

2017· article· en· W2740431598 on OpenAlexafffund
Zhuoxuan Shen, Venkata Dinavahi

Bibliographic record

VenueIEEE Power and Energy Technology Systems Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsConvertersNetwork topologyTransient (computer programming)Topology (electrical circuits)Modular designGridElectronic engineeringComputer scienceElectrical engineeringEngineeringVoltageMathematics

Abstract

fetched live from OpenAlex

HVDC projects and renewable energy sources are increasingly being integrated into traditional AC grids, forming a more complex and yet sustainable power system. This paper focuses on the comprehensive electromagnetic transient (EMT) simulation of an AC/DC grid, which is composed of CIGRÉ dc grid test system, IEEE 39-bus AC system, and wind farms. The AC/DC converters are composed by different topologies of voltage source converters, including modular multilevel converters (MMCs), three-level neutral-point-clamped converters, and two-level converters. Piecewise polynomial curve fitting is proposed to the insulated gate bipolar transistor modules in the MMC. Furthermore, hybrid modeling schemes are proposed with different levels of complexity on the AC/DC grid to obtain accurate and efficient EMT simulation on PSCAD/EMTDC®. Three-zone partition schemes based on distance, node number, and network coupling are also proposed and compared. The performance of the proposed schemes is presented and verified with a DC fault case study.

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)0.001
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

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Same venueIEEE Power and Energy Technology Systems JournalSame topicHVDC Systems and Fault ProtectionFrench-language works237,207