The Development and Testing of a Double-Loop Resolution-Level Controller for a 3ϕ WM Inverter-Fed Induction Motor
Bibliographic record
Abstract
This paper presents the development and the performance testing of a double-loop resolution-level controller (RLC) for adjusting outputs of the new wavelet modulated (WM) inverters when supplying three-phase (3phi) squirrel-cage induction motors. The basis of the proposed controller lies in the fact that adjusting the scale j of the successive dilated and translated version of the scale-based linearly-combined synthesis scaling function phitildej(t) can change the magnitude as well as the frequency of the WM outputs. The change in the scalejalone, which is achieved by single-loop RLC, can provide a limited range of magnitude variations while maintaining the output quality. This limited range is due to the fact that changing j creates a translation that is inherent in phitildej(t). To increase the range of varying the outputs of WM inverters, a double-loop RLC can be utilized such that one loop is responsible for correcting the translation as the scalejis changed. The double-loop RLC based WM inverter is realized for both MATLAB/SIMULINK simulation as well as experimental performance testing when incorporated in a 3phi capacitor-run induction motor drive. Simulation and experimental test results show effective responses to different changes in operating conditions of the tested drive. Also, some performance comparisons are conducted with the single-loop RLC as well as the conventional PI-based PWM inverter-fed 3phi drive to demonstrate the advantages of the proposed drive.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".