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Record W1562931538 · doi:10.1109/ias.2002.1042650

Managing zero sequence in voltage source converter

2003· article· en· W1562931538 on OpenAlexaff
Lianxiang Tang, Boon‐Teck Ooi

Bibliographic record

VenueConference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344) · 2003
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcGill University
Fundersnot available
KeywordsTransformerVoltage sourceVoltageSymmetrical componentsSequence (biology)Control theory (sociology)Electrical engineeringThree-phaseEngineeringComputer scienceTopology (electrical circuits)

Abstract

fetched live from OpenAlex

The 3-phase bridge voltage-source converter (VSC) which has been and continues to be the work-horse of variable-speed AC-motor drives is making penetration into the electric utility environment. In the electric utility environment, there is a greater emphasis on: (i) grounding and (ii) the need of the VSC to survive all conceivable faults. Fault analysis decomposes the unbalanced 3-phase voltages and currents into positive-, negative- and zero-sequence components. The impact of the zero-sequence on the VSC has not been well understood and a common practice is to side-step this issue by excluding the zero-sequence from the VSC at the utility/VSC interface by connecting the transformers in the Y-/spl Delta/ configuration. This paper shows that the Y-/spl Delta/ configuration can lead to destructively high over-voltage or permanent dc voltage unbalance. These problems cease to exist in transformer configurations in which the zero-sequence currents are admitted to the VSC. In order to use these transformer configurations, it is necessary to manage the zero sequence in the VSC. This paper presents the mathematical model of the zero sequence and its equivalent circuit as it affects the voltage source converter.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.244
Teacher spread0.211 · 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
GenreMethods

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

Citations27
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueConference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344)Same topicMultilevel Inverters and ConvertersFrench-language works237,207