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Record W3163947204 · doi:10.1049/hve2.12100

Steady‐state power operation region of a modular multilevel converter connecting to an AC grid

2021· article· en· W3163947204 on OpenAlexaff
Jiecong Wang, Chongru Liu, U.D. Annakkage, Xiao Li, Chenbo Su

Bibliographic record

VenueHigh Voltage · 2021
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
FundersState Grid Corporation of ChinaChina Scholarship Council
KeywordsPhasorModular designVoltage sourceTopology (electrical circuits)Power (physics)VoltageGridAC powerEquivalent circuitSteady state (chemistry)Control theory (sociology)Direct currentCapacitorComputer scienceElectrical engineeringElectronic engineeringEngineeringElectric power systemPhysicsMathematicsControl (management)

Abstract

fetched live from OpenAlex

Abstract The modular multilevel converter (MMC) has become a widely used topology for voltage source converter–based high‐voltage direct current transmission projects. To calculate the power operation region of an MMC directly and efficiently, we propose a novel steady‐state phasor model of the MMC. The model is expressed as an equivalent capacitance in series with a voltage source, where parameters are independent of alternating current (AC) electrical quantity. Based on the model, we propose an open‐loop approach to determine the operation region boundary of MMC by calculating the envelopes of the P–Q curves. The effect of MMC parameters, the circulating current control, and the short‐circuit ratio of the AC grid to the power operation region can be determined by the size and position of the power operation region. The proposed approach and analysis results were verified by an MMC/AC simulation model built in PSCAD/EMTDC.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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