Induction Machine Emulation for Extreme Weather Conditions
Bibliographic record
Abstract
Power hardware in loop technology can be used to emulate a machine's electrical behavior. This is done by power converter in a real time control loop. Stochastic extreme weather conditions lead to power systems faults. Under such conditions, the industrial machines should be able to disconnect and reconnect to the grid to have continued operations. The optimal reclosing operations to avoid vulnerability requires the knowledge of the machine terminal voltage in prior. Conventional voltage in and current out induction machine models will not provide such information. Hence, a current in voltage out model of the industrial induction machine is developed and tested with the proposed machine emulator for different fault opening and reclosing operations. High performance linear amplifiers of 100 kHz bandwidth are used for better emulating capability. The emulator test results for different reclosing operations are validated with a real machine to prove the robustness of the proposed PHIL 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 | 0.001 | 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 | 0.001 | 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".