Predictive control of four-leg power converters
Bibliographic record
Abstract
In this paper, finite control-set model predictive control of two-level and three-level four-leg voltage source converters (VSCs) is presented. The predictive current control (PCC) and predictive voltage control (PVC) schemes are presented considering grid-connected and standalone distributed generation applications respectively. The discrete-time model of load currents, load voltages, DC-link capacitors voltage and VSC terminal voltages is formulated in terms of four-leg converter switching states. A cost function is defined with the PCC scheme to minimize the error between reference and predicted load currents. Similarly, in PVC scheme, the cost function deals with the minimization of error between reference and predicted load voltages. The balancing of split DC-link capacitors voltage is considered with the three-level four-leg VSCs. The optimal switching states which minimize the cost function are chosen and applied to the four-leg VSC directly without involving linear regulators and modulation stage. The proposed PCC and PVC strategies are verified through simulation results considering single-/three-phase, balanced/unbalanced and linear/nonlinear loads.
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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".