Developing and Testing Model Predictive Control to Minimize Ground Potentials in Transformerless Interconnected Five-Level Power Electronic Converters
Bibliographic record
Abstract
This article presents the implementation and performance evaluation of a model predictive control (MPC) for three-phase (3Ψ) transformerless interconnected five-level power electronic converters (PECs). The proposed MPC employs a discrete-time model of five-level PECs to predict future values of the grid-injected currents and ground potential. Predicted values of the grid-injected currents and ground potential are used to set the reference signals to minimize a cost function, which is formulated in terms of the command and actual values of grid-injected current and ground potential. The proposed MPC is implemented for transformerless interconnected diode-clamped and flying-capacitor five-level PECs under different conditions. Test results show that the developed MPC can operate transformerless interconnected PECs to ensure accurate, dynamic, and fast responses to changes in the power delivered to the host grid. Furthermore, the MPC demonstrates a good ability to minimize ground potentials during steady state and step changes in the power delivered to the host grid.
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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 | 0.001 |
| 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".