The Wavelet-Modulation Technique for 5-Level, Power Electronic Converters—Part II: Implementation and Experimental Performance
Bibliographic record
Abstract
This article presents the implementation and experimental testing of the wavelet-modulation technique for three-phase ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\phi$</tex-math></inline-formula> ) 5-level (5L) power electronic converters (PECs). This version of the wavelet modulation technique is structured as a multiresolution analysis that can support a redundant nonuniform recurrent sampling-reconstruction of three reference-modulating signals. The reconstruction of reference-modulating signals is achieved by sets of resolution-segmented scale-base linearly combined synthesis wavelet basis functions, which are used as switching signals to activate the switching elements of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\phi$</tex-math></inline-formula> , 5L, VS dc–ac PEC. The presented wavelet-modulation technique is implemented in real time using a digital signal-processing board. Switching pulses generated by the extended wavelet-modulation technique are used to operate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\phi$</tex-math></inline-formula> , 5L, VS, diode-clamped and flying-capacitor dc–ac PECs. Experimental performance of the tested 5L dc–ac PECs is evaluated, when operated by the presented wavelet modulation, level-shifted pulsewidth modulation (PWM), phase-shifted PWM, and space vector modulation techniques. Experimental tests are conducted for linear, dynamic, and nonlinear loads fed by the tested 5L PECs. Test results show that magnitudes of output voltage fundamental components can be significantly increased, and harmonic distortions can be effectively reduced using the wavelet-modulation technique. These features are further demonstrated by performance comparisons with other techniques under similar operating 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 | 0.002 | 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".