Selective Harmonic Elimination in Cascade H-bridge Multilevel Voltage Source Inverters Using A Hybrid Optimization Algorithm
Bibliographic record
Abstract
To eliminate harmonics from output voltage waveforms of a multilevel voltage source inverter, the pulse width modulation (PWM) technique for selective harmonic elimination (SHE) can be performed at a low switching frequency, which reduces switching losses and increases the energy conversion efficiency in medium voltage and high power applications. The SHE strategy provides the optimal output voltage by maintaining the desired fundamental voltage component while eliminating lower order harmonics. This paper presents a hybrid optimization method based on the combination of the genetic algorithm (GA) and the simulated annealing (SA) to achieve a faster and more accurate optimal solution for the harmonic elimination problem. The advantages of the proposed hybrid algorithm compared to some other optimization methods include the speed that reaches the global minimum and the acceptable convergence rate considered in the performance analysis of the inverter. Simulation results show the superiority of the proposed hybrid algorithm by comparing with either GA or SA alone.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".