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Record W2024806824 · doi:10.1049/iet-gtd.2013.0327

Reduction of low‐frequency harmonics in modular multilevel converters (MMCs) by harmonic function analysis

2014· article· en· W2024806824 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Generation Transmission & Distribution · 2014
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHarmonicsFeed forwardControl theory (sociology)ConvertersHarmonicHarmonic analysisModular designTotal harmonic distortionTransformerElectronic engineeringCapacitorMATLABComputer scienceVoltageFundamental frequencyEngineeringControl engineeringElectrical engineeringPhysicsControl (management)Acoustics

Abstract

fetched live from OpenAlex

Modular multilevel converter (MMC) is troubled by the inherent second harmonic of single‐phase ac power. By adding feedforward to the modulation signal, low‐frequency harmonic voltages are significantly reduced thus overcoming an important weakness of MMC. Capacitors of sub‐modules do not have to be over‐sized. If desired for protection, transformers can be wye‐connected with grounded neutral. The reduction method is based on harmonic function analysis. In addition to deriving algebraic formulae of harmonic voltage components of ‘open loop’ control, this study makes original contributions by deriving formulae which include feedforward control. Analytical insights from the formulae have shown the way to design feedforward methods to reduce low‐frequency harmonics. Validation is by simulations using SIMULINK/MATLAB.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.199
Teacher spread0.191 · 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