Analysis and Development of Wavelet Modulation for Three-Phase Voltage-Source Inverters
Bibliographic record
Abstract
This paper presents the analysis, development, and implementation of a wavelet modulation (WM) technique for three-phase voltage-source (VS) six-pulse inverters. The WM technique is based on sampling three continuous-time (CT) sinusoidal reference modulating signals in a nonuniform recurrent manner using three sets of scale-based linearly combined wavelet basis functions. These CT signals are recovered from their samples by a three-phase VS six-pulse inverter, which is activated by three sets of synthesis scale-based linearly combined wavelet basis functions. Each set of synthesis basis functions is generated for activating one leg of the three-phase inverter in order to recover one CT reference modulating signal. The WM technique is implemented for both simulation and experimental performance testing. The performances of the WM technique are compared with those obtained using the space vector modulation, random pulsewidth modulation, and hysteresis band current control techniques under the same loading conditions. Simulation and experimental test results show that the proposed WM technique is able to switch a three-phase VS six-pulse inverter to produce outputs with significantly improved fundamental components and low harmonic contents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".