Improved Model Predictive Control for Minimizing DC-Link Capacitor Current in Parallel Dual-Inverter-Fed PMSM Drives
Bibliographic record
Abstract
Reducing the root-mean-square (RMS) current through DC-link capacitors is critically challenging in parallel dual-inverter-fed drive systems, as DC-link currents are heavily influenced by circulating currents and operating points. To address the complication, an improved finite control set model predictive control (FCS-MPC) scheme is proposed in this work. Unlike existing research, the proposed scheme accounts for circulating currents and power-sharing online when estimating DC-link ripple currents, ensuring consistent performance across a wide range of operating conditions. Moreover, the evaluation of DC-link performance is conducted over an extended prediction horizon, providing a more accurate representation of DC-link ripple current fluctuations around its average. To alleviate the computational demands imposed by a large number of switching states, a multi-rate hybrid structure is introduced. Within this structure, stator currents tracking can remain unaffected by other objectives, maintaining superior torque and speed response. Furthermore, DC-link currents prediction is performed within flexible subsets, keeping the calculation burden modest and allowing for real time application. Ultimately, the proposed scheme can achieve a 13.5% to 41.3% reduction in DC-link RMS current compared to the state-of-the-art approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".