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Record W2966853967 · doi:10.1109/isie.2019.8781460

Series Connection of VSC Modules for Offshore Wind Farm Application

2019· article· en· W2966853967 on OpenAlexaff
Xiaofan Fu, Kamal Al‐Haddad, Louis‐A. Dessaint, Abdelhamid Hamadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsOffshore wind powerConvertersGrid connectionGridWind powerSeries and parallel circuitsEngineeringElectrical engineeringSubmarine pipelineVoltageVoltage sourceHigh-voltage direct currentTopology (electrical circuits)Computer scienceElectronic engineeringDirect currentMathematics

Abstract

fetched live from OpenAlex

A DC series connection structure of offshore wind farm connecting to the main grid via HVDC link with a single centralized VSI is studied. Compared with the most existing topologies, this topology contributes to simplifying the system configuration and operation, decreasing the number of converters and removing offshore centralized converter, and increasing the DC voltage level. Moreover, to keep DC voltage stable at grid side and extract the maximum wind power from the offshore wind farm, a dual-power mode control algorithm applied to wind farm side converters and DC-bus voltage regulation with power feed-forward, to improve the dynamics response of DC voltage, used to grid side converter. Finally, a case study of 100MW offshore wind farm consisting of 50 individual 2MW PMSG-based wind turbines and which connects to the grid by HVDC system is developed in MATLAB/SPS. Simulation results verified, based on a DC series connection structure of offshore wind farm connected to the main grid via HVDC link, the whole control system works well.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

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)0.0050.001

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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