Predictive current control and DC-link capacitor voltages balancing for four-leg NPC inverters
Bibliographic record
Abstract
This paper presents a finite control set model predictive strategy to control the three-phase four-leg neutral-point-clamped (NPC) inverters. The four-leg NPC inverter delivers power to the unbalanced/nonlinear three-phase loads, and it can produce three output currents independently. The proposed method uses the discrete-time model of the inverter and load to predict the load current and capacitor voltages behavior for each valid switching state of the inverter. The control method chooses a state which generates the minimum error between the output currents and their references and also between the capacitor voltages. The neutral-leg switching frequency reduction algorithm is also proposed to improve the efficiency of the converter. Through computer simulations, the feasibility of the proposed predictive control scheme is verified; it performs well, showing a clear capacity to compensate disturbances while balancing the DC-link capacitor voltages and reducing the average switching frequency.
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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 | 0.000 |
| 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".