Investigation on uncertainty of resonant inverter system using multiple frequency modeling and Monte Carlo simulation
Bibliographic record
Abstract
The uncertainties of component tolerance, noise and perturbations place challenge in the circuit and controller design of a resonant inverter. Furthermore, in a high frequency AC distributed power systems where multiple inverter modules are paralleled, tight control of phase angle is mandatory, in addition to magnitude and frequency. This is because individual inverter output voltage phase angle and magnitude are sensitive to certain circuit uncertainty, for instance, component tolerance. Possible discrepancy of the equivalent inverter impedance due to resonant network parameters, and the DC voltage sources lead to circulating current because of phase angle or magnitude difference among modules, which will deteriorate the system efficiency and stability. Based on a general circuit model, the probability of the output voltage amplitude and phase angle of a high frequency resonant DC/AC inverter is studied through Monte Carlo simulation. It is found the phases and magnitudes of the inverters are statistically distributed with Gaussian function. Among all the possible source of randomness, the tolerance of the resonant tank components is the major attributor for output phase angle uncertainty. It is further found that the probability density function of both the amplitude and phase angle of the output voltage changes with load conditions as well as input line voltage.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".