MétaCan
Menu
Back to cohort
Record W2102747538 · doi:10.1109/tpwrd.2008.923531

Multivariable Dynamic Model and Robust Control of a Voltage-Source Converter for Power System Applications

2008· article· en· W2102747538 on OpenAlexaff
Ahmadreza Tabesh, Reza Iravani

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultivariable calculusControl theory (sociology)Voltage sourceAC powerElectric power systemEngineeringControl engineeringPower (physics)Computer scienceElectronic engineeringVoltageElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper proposes a new multivariable dynamic model and a control approach for a voltage-source converter (VSC) based on adopting instantaneous real- and reactive-power components as the VSC dynamic variable. Using power components as the dynamic variables reduces the degree of nonlinearities of the VSC model in comparison with the conventional VSC model that usesqdcurrent components as variables. Furthermore, since waveforms of power components are independent of the selectedqdcoordinates, the proposed control is more robust to the conventionally unmodelled dynamics such as dynamics of the VSC phase-locked loop system. The proposed control system regulates the VSC AC-side real- and reactive-power components, and the DC-side voltage. This paper also introduces an overcurrent limiting mechanism, based on limiting the VSC power exchange during abnormal conditions, for the proposed control system. The proposed model and control are applied to a VSC-based reactive power compensator and simulation results based on PSCAD/EMTDC software are presented.

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 score0.008

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.170
Teacher spread0.164 · 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

Citations64
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power DeliverySame topicMicrogrid Control and OptimizationFrench-language works237,207