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Record W2100834322 · doi:10.1109/tpel.2009.2023789

Harmonics Reduction in Current Source Converters Using Fuzzy Logic

2009· article· en· W2100834322 on OpenAlexaff
M. F. Naguib, Luiz A. C. Lopes

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2009
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsHarmonicsFuzzy logicConvertersSupport vector machineReduction (mathematics)Topology (electrical circuits)State (computer science)Space vector modulationAlgorithmComputer scienceElectronic engineeringElectrical engineeringMathematicsArtificial intelligenceEngineeringVoltageInverter

Abstract

fetched live from OpenAlex

High power ratings gate turn-off (GTO) thyristors are usually used with low switching frequency in high power applications current source converters (CSCs), i.e., rectifiers and inverters. Space vector modulation (SVM) technique for CSC is established by dividing ac-side line current cycle into six sectors. Each sector is divided into certain number of space vector (SV) cycles. SV cycle is divided into three states, two active and one zero state. SVM technique generates fifth and seventh harmonics (HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> ) in the CSC ac-side current when operated with low switching frequencies, i.e., low number of SV cycles. Minimal reduction in HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> was achieved by using certain states sequence inside SV cycle, and by calculating states on times at once in the middle of each SV cycle. In this paper, fuzzy logic-dependent technique for calculating states on times in SVM-CSC is proposed. First, two straightforward techniques are discussed. One calculates each state on time from SVM equations in the middle of each state effecting time period. The other calculates all states on times from SVM equations at once when the effective state is changing from one active state to the other. Both techniques give high reduction in HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> . Fuzzy logic technique adjusts states on times to give the least possible HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> with a compromise between both straightforward techniques, fuzzy logic tune states on time calculating instants in SVM-CSC. The target for fuzzy logic is to achieve fast states on times adjustment with fixed least HD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5-7</sub> in steady state for states on times. States on time's adjustment with fuzzy logic is applied also to a CSC with a small size ac-side filter. An experimental investigation is introduced.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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 teacher head, not a consensus.

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

Citations44
Published2009
Admission routes1
Has abstractyes

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