Emulation of induction machines subject to industrial grid harmonics
Bibliographic record
Abstract
Emulation is the process of imitating the behavior of a physical system by another physical system in real-time. One of the key advantages of machine emulation is, it essentially helps in assessing the machine’s dynamic behavior under various transient conditions without damaging the intended machine. Harmonics are one major power quality issue and are becoming much more important in the future. In industrial electric grids, harmonics are generated by the usage of nonlinear loads such as electric arc furnaces and switching loads. In addition, reactive power compensation devices and harmonic filters used in the power system amplify some of the harmonics at the point of common coupling. Especially in a manufacturing plant or process, the resultant harmonics in torque or speed may affect the stable operation of loads. Therefore, the usage of a machine under such conditions can be assessed by observing its dynamic behavior. This paper proposes an induction machine emulator subject to different combinations of major grid harmonics. The accuracy improvement of the emulator is done by the proposed emulator control and by choosing an accurate mathematical model. The mathematical analysis is validated with the help of simulation. The experimental results with the emulator under no load and load are validated with a real machine. This proves the dynamic performance of the proposed emulator.
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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) | 0.001 | 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 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".