CCS-MPC with Long Predictive Horizon for Grid-Connected Current Source Converter
Bibliographic record
Abstract
Model predictive control (MPC) with a long predictive horizon can offer significant benefits, especially for a power converter with high-order filters, such as virtual-impedance based active damping loop that can be eliminated. In this article, the continuous-control-set model predictive control (CCS-MPC) with a long prediction horizon is investigated for the grid-connected current-source converter (CSC). The cost function in multi-variable form is designed, which ensures power command tracking and LC filter resonance suppression simultaneously. The optimized control law is generated by a modulator to obtain a constant switching frequency. The single MPC loop can replace conventional multiloop control with reduced complexity and simplified tuning process. Simulation and experimental results show that the long horizon MPC for CSC can achieve satisfactory performance as well as improved robustness for the variation of system parameters.
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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".