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Analytical, State-Space, Full Bridge MMC model

2022· article· en· W4312780966 on OpenAlexaff
Dragan Jovcic, Jean Mahseredjian

Bibliographic record

Venue2022 IEEE Power & Energy Society General Meeting (PESGM) · 2022
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Bridge (graph theory)State-space representationHarmonicEigenvalues and eigenvectorsState spaceAutoregressive modelHarmonic analysisComputer scienceModulation (music)Modulation indexRange (aeronautics)Topology (electrical circuits)EngineeringMathematicsAlgorithmElectronic engineeringPhysicsPulse-width modulationVoltageControl (management)Artificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents the development of a small signal analytical state-space model for a Full Bridge MMC converter. Building on the existing Half Bridge MMC analytical model, all new model equations are derived considering the new control variable, DC modulation index Mdc. The model is of 29thorder, and it is developed in 3 rotating dq coordinate frames: zero sequence, fundamental frequency and second harmonic. The model is verified against detailed non-linear EMTP model for a 1 GW, 640 kV MMC test system. The verification demonstrates good accuracy for all model variables considering a range of inputs on the reference and disturbance signals. The developed model is employed to study eigenvalues position as the Mdc controller gains change, and it is concluded that Mdc controller improves damping of the dominant oscillatory mode.

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: none
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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