Model-Based Closed-Loop Control for High-Power Current Source Rectifiers Under Selective Harmonic Elimination/Compensation PWM With Fast Dynamics
Bibliographic record
Abstract
Selective harmonic elimination/compensation PWM (SHE/SHC-PWM) is widely used in current source rectifiers (CSRs) under normal/distorted grid conditions due to its best line current harmonic performance with reduced switching losses. However, due to the off-line nature of SHE/SHC-PWM that operates over fundamental frequency, its dynamic performance under closed-loop control is usually not satisfactory compared with the space-vector modulation or carrier-based PWM (CB-PWM) for CSR. To improve the closed-loop dynamics of CSR under SHE/SHC-PWM, a model-based closed-loop control scheme is proposed in this article. In case of transient when the dc current reference of CSR changes, the new input references of the SHE/SHC-PWM module, i.e., the modulation index and delay angle, are directly calculated and updated in real-time based on the mathematical model of CSR derived in this article. As a result, the dynamic performance for dc current tracking under closed-loop SHE/SHC-PWM operations can be improved greatly due to this added model-based feedforward path, while the conventional feedback controller is still adopted but only for steady-state error corrections. The application of the proposed method on back-to-back current source converter (CSC) motor drives is also introduced. Simulation and experimental results validate this proposed control for CSR with fast dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".