Indirect Minimization of Common-Mode Voltage with Finite Control-Set Model Predictive Control in a Five-Level Inverter
Bibliographic record
Abstract
The cost function with weighting factor has been adopted in the conventional finite control-set model predictive control (FCS-MPC) methods to directly minimize the common-mode voltage (CMV) of multilevel inverters (MLIs), which further affects the MLI’s output current and voltage quality. These methods also need higher execution time to implement in real-time controllers. In this paper, a new FCS-MPC philosophy is presented to minimize the CMV indirectly, thereby eliminating the need of weighting factors and their impact on voltage and current harmonic distortions. Also, the proposed FCS-MPC is designed to achieve the control objectives of each phase by using an independent cost function, resulting in shorter execution time. The proposed method applied to a five-level MLI (5L-MLI), and the corresponding discrete-time models are developed by using Heun’s integration method. The efficacy of the proposed FCS-MPC method is demonstrated through a scale-down laboratory prototype. Furthermore, the experimental comparison studies with the conventional Heun’s integration method-based FCS-MPC methods with and without CMV minimization are presented.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".