Induction Machine Emulation under Asymmetric Grid Faults
Why this work is in the frame
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Bibliographic record
Abstract
Machine emulation is the concept of developing a virtual machine in real time with power-hardware-in-the-loop (PHIL) technology. This process of emulation allows testing the variable speed drive or drive inverter or the performance of grid without using a real machine. In this paper, the machine emulated is a three-phase induction motor (IM) fed from the grid. The common asymmetric-grid faults namely unbalanced voltages, line-to-line (L-L) and line-to-neutral (L-N) faults are applied at the input terminals of a virtual induction machine. The main contribution of this paper is a detailed analysis of machine's asymmetric fault behavior and its cause by mathematical derivations and simulations for the process of imitation by a virtual machine. This is being done by corresponding step by step improvement in emulator controller design. The experimental results with the emulator are validated with a real machine and also with Matlab simulation to prove the dynamic performance and accuracy of the proposed novel emulator.
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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 it