Harmonic Suppression Strategy in Single-phase SPWM Based on Mixed Integer Nonlinear Programming
Bibliographic record
Abstract
In single-phase bipolar SPWM inverter circuit, the output voltage contains high content of harmonic components. To solve this problem, we build a model of output voltage in single-phase SPWM bipolar inverter based on SPWM theory and Fourier series theory. Through theoretical analysis, calculation and plotting, we work out the distribution of harmonic in output voltage, investigate the relationship between amplitude modulation (AM) depth and harmonic components and finally determine a proper amplitude modulation depth to efficiently reduce harmonic components. To further reduce harmonic components of output voltage, we use LC low-pass filter. Then, based on the mathematical model of the filter and the distribution law of harmonics, we establish a mixed integer nonlinear programming to fully suppress harmonics and reduce switching losses. After searching for two times based on genetic algorithm, we work out the best frequency-modulation ratio and LC low-pass filter parameter. Finally in simulation, total harmonic distortion is reduced from 213.47 percent to 0.16 percent after harmonic suppression strategy implemented. The result has proved the effectiveness of the harmonic suppression strategy we applied, which can be used as a reference for output voltage harmonic elimination in single-phase SPWM inverter.
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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 | 0.000 |
| 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 | 0.001 | 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".