A Novel Control Strategy for Online Harmonic Compensation in Parametrically Unbalanced Induction Motor
Bibliographic record
Abstract
Inverter-fed motor drive operating an unbalanced induction motor (IM) has high harmonic content which inflicts large torque ripple on the load. Dead short circuit and incipient or partial short circuit in motor stator windings lead to asymmetry in the machine parameters. Consequently, an imbalance in the voltage supply worsens the condition deteriorating the optimal performance of the drive-based motor due to the injection of both increased time and spatial harmonics. It is of primary importance that these discrepancies are taken care of while modeling a more fault tolerant, reduced harmonics drive system. This paper proposes a novel control strategy to minimize torque ripple by considering the time harmonics produced due to imbalance in inverter voltage and parameters of the faulty IM, and the estimated space harmonics from the measured magnetic flux density in a transient magnetic phenomenon. The proposed control strategy has been implemented on an unbalanced aluminum-rotor IM with online monitoring of unhealthy conditions and feeding it to the harmonic compensation block of the drive system.
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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".