Efficient Rectified Stator Currents Hysteresis Control of the Induction Motor Drive and Flux Optimization Using Fuzzy Logic
Bibliographic record
Abstract
The induction machine (IM) is a nonlinear, multivariable, strongly coupled system.Therefore, it is necessary to achieve a decoupling between flux and torque.The vector control technique is the one that gives better performance.To have high dynamic responses and better torque control, the machine must be supplied with sinusoidal currents.The present work contributes to improving the efficiency of a flux-oriented indirect control (IFOC) of an induction motor associated with a new hysteresis inverter.The Induction motors have good efficiencies when operating at full load.However, at lower than rated loads, which is a condition that many machines experience for significant portion of their service life, the efficiency is greatly reduced.To improve the efficiency of the existing motor it is important to regulate the flux of the motor in the desired operating range.This paper proposes the analytical approach of minimizing copper losses for an induction motor and energy efficient control strategy based on fuzzy logic using Matlab / Simulink®.This parameter is used to determine an optimal rotor flux reference it has the goal of maximizing the efficiency for each given load torque.The proposed fuzzy controller adjusts the electromagnetic torque, to give the optimized flux by minimizing losses.
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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 | 0.000 | 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".