Development and Testing of Wavelet Modulation for Single-Phase Inverters
Bibliographic record
Abstract
This paper introduces a novel modulation technique for single-phase (1 phi) voltage-source (VS) dc-ac power inverters. The proposed modulation technique is based on a newly designed scaling function that is capable of supporting a nonuniform recurrent sampling process. This scaling function generates sets of basis functions that span spaces, of which a collection constructs a nondyadic-type multiresolution analysis. Furthermore, the newly designed scaling function has a dual synthesis scaling function that is designed to reconstruct continuous-time signals from their nonuniform recurrent samples. The proposed wavelet modulation technique is implemented using a MATLAB code that generates switching pulses to activate a SIMULINK model of a 1 phi VS four-pulse (H-bridge) inverter. Several performance tests are conducted for the proposed wavelet-modulated (WM) inverter, when supplying linear, nonlinear, static, and dynamic loads. The results of these tests show significant reduction of the output harmonics along with high magnitude of the output fundamental component. The efficacy of the proposed WM inverter is further demonstrated through a comparison of the random pulsewidth modulation (RPWM) and the pulsewidth modulation (PWM) inverters for supplying the same loads. The comparison results show that the proposed WM inverter is capable of producing outputs with higher quality than the RPWM and PWM inverters.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".