Optimization of Analytical TSFs for DC-Link Current Reduction in Switched Reluctance Motors
Bibliographic record
Abstract
The large dc-link current is a known issue in switched reluctance motor (SRM) drives, which often demand the use of a bulky dc-link capacitor. However, control techniques can be designed and optimized to lessen this issue. In this context, this article proposes the optimization of analytical torque sharing functions (TSFs) for dc-link current reduction in SRMs. Initially, the analytical TSFs are described, and the importance of adequate parameter selection is highlighted. Next, an optimization procedure based on the nondominated sorting genetic algorithm II is proposed to determine the optimal turn-onand overlap angles by solving a multiobjective optimization problem considering torque rms error and dc-link rms current as objectives to be minimized, something not previously reported in the literature. The pareto fronts for different operating conditions are presented, including both soft and hard chopping operation, as well as different sampling frequencies. Then, an approach for selecting a solution from within the pareto front is described, enabling the result from the pareto front that yielded the desired tradeoff between torque RMSE and dc-link current to be identified. Experimental results are provided to support the effectiveness of the proposal. A comparison between three different cases is shown, highlighting the tradeoff between objectives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".