A Composite Selective Harmonic Elimination Model Predictive Control for Seven-Level Hybrid-Clamped Inverters With Optimal Switching Patterns
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Bibliographic record
Abstract
A composite strategy that combines selective harmonic elimination pulsewidth modulation (SHE-PWM) and model predictive control (MPC) for seven-level hybrid-clamped (7L-HC) inverters is presented in this article. By introducing the unified SHE formulation, all seven-level switching patterns and corresponding switching angles can be obtained simultaneously. Therefore, the optimal switching pattern with the designed optimization goal of each modulation index can be evaluated, and the best expected output performance is achieved. For the voltage balancing issue of 7L-HC, MPC is adopted to control the dc-link and flying capacitors. After receiving the output voltage level signal from the SHE-PWM modulator, the optimal switching state that belongs to the received output voltage level that minimizes the cost function is selected by the MPC module, where the cost function is designed to simultaneously balance capacitor voltages and reduce the switching frequency. Dynamic weighting factors with variable band limits are also proposed to further improve the system performance. The potential industrial application of high-power motor drive is used as an example in designing the key parameters for both SHE and MPC parts. Simulation and experimental results confirmed the validity of this composite SHE-MPC strategy in reducing the switching frequency and improving harmonic performances while keeping capacitor voltages well balanced.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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