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Record W2903111148 · doi:10.1109/access.2018.2884506

Real-Time Simulation of Hybrid Modular Multilevel Converters Using Shifted Phasor Models

2018· article· en· W2903111148 on OpenAlexaff
Yingdong Wei, Dewu Shu, Xiaorong Xie, Venkata Dinavahi, Zheng Yan

Bibliographic record

VenueIEEE Access · 2018
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
FundersScience and Technology Project of State GridNational Key Research and Development Program of ChinaState Grid Corporation of ChinaNational Natural Science Foundation of China
KeywordsPhasorThévenin's theoremModular designConvertersComputer scienceElectronic engineeringVoltageEquivalent circuitControl theory (sociology)EngineeringElectrical engineeringElectric power systemPower (physics)Control (management)Physics

Abstract

fetched live from OpenAlex

The real-time simulation of modular multilevel converter (MMC) is essential to the evaluation and validation of its control and protection systems. Moreover, the dynamics at both sub-module level and system level are expected from the real-time simulations of MMCs. To achieve this objective, this paper proposes the shifted phasor modeling (SPM) of the MMC by representing each sub-module with a Thevenin equivalent circuit that is derived using shifted phasors with improved accuracy. The efficiency of the SPM is guaranteed by modeling each arm as a switch-dependent Thevenin equivalent circuit. The computational burden remains almost unchanged even when the number of sub-modules increases considerably. The proposed model is materialized using field programmable gate array. And, thus the real-time simulation of MMC-based DC grids can be realized to capture the dynamics at the system level as well as the sub-module level. The effectiveness of this paper has been validated in terms of both accuracy and efficiency on a two-terminal MMC-based low-voltage direct current system.

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 score0.424
Threshold uncertainty score0.528

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 communication0.0000.001
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 head0.052
GPT teacher head0.307
Teacher spread0.255 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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