Induction Machine Emulation under Asymmetric Grid Faults
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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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 | 0.001 |
| 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".