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Record W2734873919 · doi:10.1109/eeeic.2017.7977709

A hybrid switching VSC based converter for reactive power compensation in utility grid

2017· article· en· W2734873919 on OpenAlexaff
K. S. Amitkumar, G. B. Mahmud, P. Tamanwe, S. Navjot, Akshay Kumar Rathore, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsConcordia University
Fundersnot available
KeywordsCompensation (psychology)AC powerComputer scienceControl theory (sociology)GridVoltagePower (physics)Static VAR compensatorVoltage sourceKey (lock)Electronic engineeringControl (management)EngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

In high power applications such as static synchronous compensator (STATCOM) used for reactive power compensation, it is essential to reduce semiconductor losses and associated cost to a minimum value while maintaining desired key attributes. This paper presents a new hybrid switching STATCOM that addresses the semiconductor device losses while ensuring the usual STATCOM operating criterion. Design and control of the hybrid STATCOM are presented followed by real-time simulation results to validate the proposal. Losses are calculated and the results are compared with a conventional two-level voltage source converter (VSC) based STATCOM to quantify the benefits of the proposed hybrid STATCOM. It is demonstrated that the proposed hybrid STATCOM achieves close to 10% lower semiconductor losses compared with a conventional two-level STATCOM.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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