Optimal Voltage Level-Based Sequential Predictive Current Control With Reduced Complexity for Multilevel Inverters
Bibliographic record
Abstract
The conventional Euler-based sequential predictive current control (SPCC) methods for multilevel inverters (MLIs) have aimed to minimize computational complexity, resulting in higher flying capacitor (FC) voltage ripples and poor transient response. These methods typically employ either cost function with weighting factors or offline selection of voltage vectors to reduce the common-mode voltage (CMV). However, improper weighting factor selection and the presence of CMV term in ac current models negatively impact MLI harmonic performance. To address these issues, a new SPCC formulation is proposed, enabling direct estimation of the optimal voltage level based on reference ac currents. This approach eliminates the need for a cost function in ac currents control and simultaneously reduces both computational complexity and CMV. By removing ac current model's dependency on CMV, the proposed formulation further enhances MLI harmonic performance. The mathematical formulation of the proposed SPCC is presented for a four-level inverter (FLI) with both passive and motor drive loads in this study. During the formulation, the Heun's integration method is employed to develop FLI's FC voltage discrete-time models. The performance of the proposed SPCC is demonstrated experimentally on a dSPACE-DS1103 controlled FLI with passive load laboratory prototype. It is further compared with the conventional SPCC methods and their performances are evaluated comprehensively on the laboratory prototype.
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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".