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Record W4409129952 · doi:10.1109/tpwrd.2025.3551546

Comprehensive Full-Scale Converter Wind Park Initialization for Electromagnetic Transient Studies

2025· article· en· W4409129952 on OpenAlexaff
J. A. Ocampo-Wilches, Jean Mahseredjian, Keijo Jacobs, Ahda P. Grilo, Haoyan Xue

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh-Voltage Power Transmission Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTransient (computer programming)InitializationWind powerScale (ratio)Electrical engineeringTransient analysisEngineeringMarine engineeringComputer scienceAerospace engineeringTransient responsePhysics

Abstract

fetched live from OpenAlex

This paper proposes a comprehensive method for initializing the electromagnetic transient models of full-scale converter wind parks. The method uses the ac load-flow solution to initialize the mechanical model, the electrical components, the machine, the converter and the control systems. The effectiveness of the method is demonstrated through EMT simulations of three different power system benchmarks: an aggregated WP connected to a small transmission grid, a detailed WP model with wind turbines connected to a small transmission grid, and a large-scale transmission grid with ten different aggregated WPs. The results show that the proposed method reduces computing times required to reach steady-state and consequently accelerates overall simulations.

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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.256
Teacher spread0.238 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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