DNN-Based Predictive Magnetic Flux Reference for Harmonic Compensation Control in Magnetically Unbalanced Induction Motor
Bibliographic record
Abstract
A stator inter-turn fault occurring in one of the phases of a three-phase induction motor (IM) gives rise to high harmonics distortion in air-gap flux density, increased torque ripple, temperature rise in the stator windings, and mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated when compared to that during the normal motor operation. An incipient stator fault leads to variation of machine's parameters, causing malfunction of the motor drive. Hence, it is of significant importance to detect the incipient fault before complete motor breakdown occurs. In this paper, a novel magnetic flux reference predictive method for control has been presented by using a harmonic compensation block in coordination with deep neural network (DNN) as a feedforward method to continue safe operation of motor after occurrence of incipient stator fault. This method takes into account both time and space harmonics discrepancies produced due to the fault. The proposed method has been implemented on a 7.5 hp IM using online observer of unhealthy conditions and compensated using DNN predictive methodology.
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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) | 0.000 | 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) | 0.000 | 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".