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Record W2593178854 · doi:10.1049/iet-gtd.2016.1664

Interfacing an EMT‐type modular multilevel converter HVDC model in transient stability simulation

2017· article· en· W2593178854 on OpenAlexaff
Xuekun Meng, Liwei Wang

Bibliographic record

VenueIET Generation Transmission & Distribution · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsInterfacingModular designTransient (computer programming)Computer scienceHVDC converter stationType (biology)Stability (learning theory)HVDC converterEngineeringElectronic engineeringElectrical engineeringVoltageComputer hardwareProgramming languageGeology

Abstract

fetched live from OpenAlex

This study proposes a hybrid electromechanical and electro‐magnetic transient (EMT) simulation algorithm, which interfaces the EMT‐type modular multilevel converter (MMC) high‐voltage direction current (HVDC) model into transient stability simulation. The proposed hybrid simulation algorithm offers high fidelity simulation results for the fast transient phenomenon of the MMC HVDC using the EMT program (EMTP). At the same time, the hybrid simulation algorithm utilises the transient stability program (TSP) to represent a large external AC network connected to the MMC HVDC. Thus, the proposed hybrid simulator combines the advantages of the TSP and EMTP simulations and offers accurate and efficient solutions for the integrated AC and HVDC systems. A case study of MMC HVDC system connected to 10‐generator 39‐bus New England AC System is simulated using hybrid and full‐EMTP simulations in PSCAD/EMTDC. The simulation results demonstrate the proposed hybrid simulator produces accurate simulation results compared to the full‐EMTP solutions.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.070
GPT teacher head0.295
Teacher spread0.225 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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