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Modeling and Step Response Analysis of Back-to-Back VSC for LFAC Transmission

2019· article· en· W3018208264 on OpenAlexaff
Okechukwu Efobi, Wei Li, Ani Gole, Mukesh Kumar Das

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGridOffshore wind powerFault (geology)HVACHVDC converterCompensation (psychology)Voltage sourceMaximum power transfer theoremHVDC converter stationTransmission (telecommunications)Transmission systemEngineeringElectronic engineeringElectrical engineeringComputer scienceControl theory (sociology)Power (physics)Wind powerVoltagePhysicsMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Low frequency AC (LFAC) is being proposed as an intermediate transmission technology between HVAC and HVDC. It could be attractive as a means for connecting offshore wind farms to the grid, in lieu of VSC-HVDC. Its single converter station could even be located onshore. Because it could operate at a fraction of 50/60 Hz frequency, LFAC could transfer significantly more power and for longer distances, without compensation, than HVAC. Unlike HVDC, LFAC could have the advantage of employing conventional AC circuit breakers for fault current interruption. Thus, it could be readily used for multi-terminal connections. This paper presents state space modeling of an ideal back-to-back VSC (frequency converter). Eigenvalues of the linearized model are used for stability and step response analysis of the converter. PSCAD/EMTDC simulations are then used to verify the accuracy of the derived model. This state space model could be applied in the analysis of LFAC systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.493
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.236
Teacher spread0.222 · 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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

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