Optimization-Based Capacitor Current Reduction in Switched Reluctance Generators Operating under Single-Pulse Control
Bibliographic record
Abstract
Switched reluctance generators (SRGs) are attractive alternatives for wind energy conversion systems. However, the drive suffers from large RMS capacitor currents, which increase converter size, cost, and reliability concerns. While capacitor current mitigation for SRGs has been studied, limited attention has been devoted to the single-pulse region. In this context, this paper proposes an optimization-based method for capacitor current reduction in single-pulse-controlled SRGs. An exhaustive search algorithm evaluates different firing angles under both hard chopping and soft chopping excitation, considering RMS capacitor current, RMS phase current, and torque ripple as performance metrics. A normalized cost function is employed to identify optimal trade-off between capacitor current reduction and torque ripple. The resulting parameters are stored in one-dimensional lookup tables for straightforward digital implementation with low memory and computational requirements. Simulation results demonstrate that the proposed method consistently reduces capacitor current while maintaining acceptable torque performance. In addition, a comparison with a minimal torque-ripple approach is provided, further emphasizing the capacitor current reduction capabilities of the proposal.
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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) | 0.000 | 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) | 0.001 | 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".