Model Predictive Control With Inherent CMV Reduction Capability for Multilevel Inverters
Bibliographic record
Abstract
The conventional model predictive control (MPC) methods use either a cost function with weighting factors or an offline selection of voltage vectors to minimize the common-mode voltage (CMV) in three-phase multilevel inverters (MLIs). However, such methods significantly affect MLIs harmonic performance. Moreover, these methods are designed based on the three-phase modeling and implementation philosophy. Hence, they are difficult to extend for multiphase systems directly without affecting the computational complexity. In this article, an MPC method capable of minimizing CMV inherently is proposed. The proposed MPC is formulated by following the per-phase implementation philosophy. Moreover, the maximum number of predictions in the proposed MPC is limited to the number of output levels of MLIs only. Consequently, it results in a significant reduction in computational complexity. The proposed method is applied to a four-level MLI, and its performance is demonstrated through experimental studies on a dSPACE-controlled laboratory prototype. Furthermore, a performance comparison of the proposed and the conventional MPC methods is assessed in terms of CMV, total harmonic distortion, and computational complexity.
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How this classification was reachedexpand
Full frame distilled prediction
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)
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 teacher head, 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".