Direct Predictive Current Control of a New Five-Level Voltage Source Inverter
Bibliographic record
Abstract
This article proposes a direct predictive current control approach to regulate the flying capacitor voltages along with the output currents in a new five-level voltage source inverter under a wide range of output frequencies and load power factors. Also, the proposed approach maintains the flying capacitor voltage ripples well below the desired limits (10%-15%) throughout the operating range. The sampled data model of a new five-level voltage source inverter is developed to predict the future behavior of the control variables such as output current, and flying capacitor voltage and its ripples. A three-objective cost function with reference and predicted control variables is formulated. The cost function is evaluated for all possible switching states, then an optimum switching state which gives the low cost value is selected. The optimum switching state is applied to the converter in one sampling interval. The feasibility of the proposed control method is verified on a new five-level voltage source inverter by using MATLAB/Simulink simulations and experimental studies on a scaled-down laboratory prototype with dSPACE Microlab Box. Furthermore, the performance of the proposed approach is analyzed in terms of voltage and current harmonic distortion, and flying capacitor voltage ripples at different current magnitudes, output frequencies, and load power factors.
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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".