Experimental Performances of the Single-Phase Wavelet-Modulated Inverter
Bibliographic record
Abstract
This paper presents the real-time implementation and experimental performances of the wavelet-modulation technique for single-phase voltage-source (VS) inverters. The wavelet-modulation technique is realized through constructing a nondyadic-type multiresolution analysis, which supports sampling of a sinusoidal reference-modulating signal in a nonuniform recurrent manner, then reconstructing it using the inverter-switching actions. The required nonuniform recurrent sampling is carried out by using dilated and translated sets of wavelet basis functions, which are generated by the scale-base linearly combined scaling function. The reconstruction of the sampled signal is accomplished by using dilated and translated sets of wavelet basis functions, which are generated by the scale-base linearly combined synthesis scaling function. The dilated and translated sets of wavelet basis functions used in the reconstruction are employed as switching signals to activate the inverter-switching elements. The wavelet-modulation technique is implemented in real time by using a digital signal processing board to generate switching pulses for a single-phase VS <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> -bridge (four-pulse) inverter. Experimental performances of the single-phase inverter, which is operated by the wavelet-modulation technique are investigated while supplying linear, dynamic, and nonlinear loads with different frequencies. Experimental test results show that high magnitude of fundamental components and significantly reduced harmonic contents of the inverter outputs can be achieved using the wavelet-modulation technique. The efficacy of the developed modulation technique is further demonstrated through performance comparisons with the pulsewidth- and random-pulsewidth-modulation techniques for similar loading conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".