Efficiency improvement and torque ripple minimisation of four‐phase switched reluctance motor drive using new direct torque control strategy
Bibliographic record
Abstract
The direct torque control (DTC) strategy is one of the most effective techniques, used to control the switched reluctance motor (SRM) with improved dynamic performance and reduced torque ripple. However, this approach draws a higher source current due to an extension of the phase current into the negative torque region, which lowers the net torque per ampere ratio. This study proposes a new DTC strategy for SRM to overcome this issue by modifying the partition of the sectors and appropriate voltage vector selection. Therefore, the proposed method improves the drive efficiency while minimising torque ripple. To implement this method, a non‐linear machine model is developed using the torque and flux characteristics obtained from experimental studies on a four‐phase 8/6 SRM. The proposed DTC scheme is implemented on a digital control platform and power loss calculations are performed to evaluate the drive efficiency. Test results show that the proposed DTC method has improved performance in terms of efficiency and torque ripple under various operating conditions in comparison to the conventional DTC strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".