Intelligent flux predictive control through online stator inter-turn fault detection for fault-tolerant control of induction motor
Bibliographic record
Abstract
Inter-turn insulation failure in induction motor results in complete or developing winding short circuit. Stator winding fault leads to an unbalance in the three phases of the motor leading to a faulty induction motor with increased time and space harmonics of flux. This can lead to uneven distribution of air gap flux and increase in torque ripple. The condition is worsened due to an unbalance in the voltage supply depreciating the optimal performance of the drive-based It is of primary importance that the aforementioned discrepancies are taken care of while modelling a more fault tolerant motor drive system with faster processing and lower response time. This paper proposes a novel control technique to reduce the unbalance in the motor due to stator fault by taking into account the air-gap flux developed in the motor and harmonics generated. An improved swarm optimization algorithm has been used in order to efficiently predict the flux reference for the stator-flux controlled motor drive. The proposed detection scheme has been implemented on an aluminum-rotor induction motor with incipient stator inter-fault with the help of online monitoring of unhealthy conditions and using it as a feedback for the drive system, thereby a robust online detection of fault and a stable fault control system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".