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Record W2998739879 · doi:10.1049/iet-rpg.2019.0513

Harmonic circulation and DC voltage instability mechanism of parallel‐SVG system

2020· article· en· W2998739879 on OpenAlexaff
Jikai Chen, Hui Shao, Yiping Cheng, Xiaozhe Wang, Guoqing Li, Chu Sun, Qirong Jiang, Jiangchao Qin

Bibliographic record

VenueIET Renewable Power Generation · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
FundersDepartment of Science and Technology of Jilin Province
KeywordsScalable Vector GraphicsMechanism (biology)HarmonicInstabilityCirculation (fluid dynamics)VoltageControl theory (sociology)Computer sciencePhysicsElectrical engineeringMechanicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

A parallel static var generator (SVG) system based on cascaded H‐bridge topology in wind afflux station is analysed in this study to reveal the mechanism of harmonic current and DC voltage instability due to the phase difference between the PWM carriers. First, the inherent relation of harmonic current and low‐frequency disturbance is revealed by carrier‐phase‐shift pulse‐width‐modulation (CPS‐PWM) theory. Second, to identify the negative impact of low‐frequency disturbance, a small‐signal circuit model is built to research the electric coupling relation between SVGs and the grid, then pointing out the main factors which affect the harmonic circulation among the two‐paralleled SVGs. Third, the output impedance and the transfer function of SVG give an insight into the interaction between SVG and low‐frequency resonance characteristics, based on which a virtual resistance method is proposed to shape the resonant peak. Finally, theoretical analysis and system experiment prove that the proposed control strategy can rapidly attenuate low‐frequency resonant peaks of the system, avoiding the DC voltage instability in the SVG system when harmonic circulation occurs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.182
Teacher spread0.169 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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