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Harmonic Analysis of SPWM Wave for Single-Phase Bridge Inverter

2023· article· en· W4386076761 on OpenAlexaff
Shuang Xu, Ling Pang, Jinghua Zhou, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsEnergie NB Power (Canada)University of New Brunswick
FundersNorth China University of Technology
KeywordsSine waveHarmonicFourier seriesFourier transformPulse-width modulationHarmonic analysisModulation (music)Frequency modulationAmplitude modulationFourier analysisPhysicsMathematicsAcousticsMathematical analysisComputer scienceVoltageBandwidth (computing)Telecommunications

Abstract

fetched live from OpenAlex

Two-level single-phase bridge inverters are widely used in distributed energy systems due to its topological symmetry and ease of modulation. The harmonic contents of modulated output waves are critical performance index in quantitatively determining the superiority or inferiority of a modulation strategy and in optimizing the modulation and filter parameters. For past decades, harmonic analyses on sinusoidal pulse width modulated (SPWM) waves of single-phase bridge inverters are mostly based on the double Fourier series representation, which is on the premise that SPWM wave is a two-dimensional periodic function of two variables with different frequencies. However, this premise is more tenable for pulse amplitude modulation (PAM) than for SPWM, since PAM is the product of a line-frequency sine wave and a carrier-frequency rectangular wave, whereas SPWM wave is the compared result of a line-frequency sine wave and a carrier- frequency triangular wave. This paper studies the double Fourier series representation of SPWM wave from double Fourier integral essence, develops a harmonic analysis method for SPWM wave with the aid of fast Fourier transform (FFT), correlates the harmonic spectra of PAM and SPWM, and finally, further analyzes the feasibility of double Fourier series on SPWM wave.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.481

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 head0.085
GPT teacher head0.279
Teacher spread0.193 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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