Performance testing of a 2 Loop RLC WM inverter-fed induction motor drive
Bibliographic record
Abstract
This paper presents the development and performance testing of a 2 loop resolution-level controlled (RLC) WM inverter-fed three-phase (3phi) squirrel-cage induction motor (IM) drive system. The proposed RLC WM inverter-fed IM drive is based on controlling the WM inverter output voltages using both a phase-shift thetas and the maximum value of the scale J. These two parameters are employed to adjust the locations and durations of the successive dilated and translated versions of the scale-based linearly-combined synthesis scaling function phij(t) that is responsible for activating the inverter switching elements. The proposed control approach is designed so that one loop is to hold J constant for small changes in thetas in response to steady-state deviations in the motor speed, while the second loop is responsible for adjusting thetas to vary the WM inverter output voltages. This control approach can offer stable and effective responses to both steady-state and step changes over a wide range of motor operating speeds. The 2 loop RLC WM inverter-fed IM drive is realized for both simulation as well as experimental performance testing under different operating conditions. Simulation and experimental performance test results show effective responses to different changes in operating conditions of the tested drive. Also, some performance comparisons are conducted with a fuzzy-logic and a proportional-integral controllers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".