Calculation of Optimal Switching Angles for a Multilevel Inverter Using NR, PSO, and GA- a Comparison
Bibliographic record
Abstract
Currently, multilevel inverters have been increased the number of applications in the industrial sector and renewable energy sources. Among its characteristics, the most remarkable are modular design, high performance, and low harmonic distortion in the output voltage waveform. For this paper, a single-phase Cascade H-Bridge Multilevel Inverters (CHB-MLI or CMLI) topology with independent DC sources, has been selected for the case study. Analyzing three scenarios: 5-level, 7-level, and 9-level applying the concept of the Optimized Harmonic Stepped-Waveform (OHSW) and comparing the results between the Selective Harmonic Eliminated-Pulse Width Modulation (SHE-PWM) and the Optimal Minimization of the Total Harmonic Distortion (OMTHD) are also presented. To compare the results obtained with classical and nature-inspired optimization methods, three techniques are used to solve transcendental nonlinear equations for the problem of Total Harmonic Distortion (THD) minimization: Newton Raphson (NR), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), which have been widely used for the problems of THD minimization in multilevel inverters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".