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Record W2995881083 · doi:10.1109/iemcon.2019.8936301

An Average Value Model of Hybrid Cascaded Multilevel Voltage Source Converter for Accelerated EMT Simulation

2019· article· en· W2995881083 on OpenAlexaff
Jintao Han, Levi Bieber, Xuekun Meng, Liwei Wang, Wei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOpal-Rt Technologies (Canada)Okanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsConvertersModular designTransient (computer programming)VoltageComputer scienceElectronic engineeringFault (geology)Voltage sourceRenewable energyCo-simulationTransmission (telecommunications)Topology (electrical circuits)EngineeringElectrical engineeringSimulationTelecommunications

Abstract

fetched live from OpenAlex

The voltage source converters (VSC) based high voltage direct current transmission systems become increasingly popular for efficient transmission of large-scale renewable energy over long distances. Recently, the hybrid cascaded multilevel converter (HCMC) is proposed to further improve the converter efficiency, compactness, and fault resilience compared to the traditional modular multilevel converters (MMCs). The efficient and accurate simulation of the HCMC in electromagnetic transient (EMT) programs play an important role for the converter control and design. The previous research works focus on numerically efficient and accurate models of the MMCs, but very few on those of the HCMC. This paper proposes an average value model (AVM) of the HCMC, which significantly improves the simulation efficiency while maintaining the simulation accuracy. The proposed AVM is validated against the detailed equivalent model (DEM) for dynamic transients. The simulation results by the proposed AVM demonstrate good modeling accuracy. The simulation speed of the proposed AVM is independent of the number of submodules, which is very desirable for the HCMC with large numbers of submodules.

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 score0.555
Threshold uncertainty score0.485

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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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
Published2019
Admission routes1
Has abstractyes

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