Model Predictive Control With Reduced Common-Mode Current for Transformerless Current-Source PMSM Drives
Bibliographic record
Abstract
The integrated dc-link choke can replace the bulky transformer in current-source converter (CSC)-fed motor drive systems to bear the common-mode voltage (CMV). However, the common-mode (CM) resonance will be excited by the specific harmonics in the CMV, which causes excessive CM current in the loop. Moreover, due to the high-order filter, the LC/CL resonance is prone to be induced at the rectifier and inverter sides, respectively. In this article, the model predictive control (MPC) scheme is proposed to tackle the CM resonance and filter resonance simultaneously. In the low-speed region, besides restraining the peak-to-peak (PTP) magnitude of the CMV at the rectifier side, the third-order harmonic of the CMV generated by the inverter is extracted and then penalized in the cost function to further suppress the CM current. With the increase of the motor speed, the control objective of the inverter side controller switches to the PTP magnitude of CMV suppression because of the alleviation of the CM resonance. The capacitor voltage of both rectifier and inverter sides are regulated via the cost function as well, which can mitigate the current harmonic distortion and improve system stability. The simulation with 1MVA rated power and the scaled-down experiment shows that the proposed scheme can suppress both the CM resonance and the LC/CL resonance effectively with a low switching frequency.
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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".