An Improved Analytical Model for Power-Hardware-in-the-Loop Emulation of Induction Machine Stator Inter-Turn Faults
Bibliographic record
Abstract
This article proposes an improved voltage-in and current-out based analytical model for emulating an induction machine's stator inter-turn fault behavior. The proposed model is compared with analytical models available in the literature. A voltage-behind-reactance (VBR) model is also developed to model stator inter-turn faults for the inverter fed motor drive applications. This methodology has advantages for particular fault types with open-loop control. The developed VBR model is simulated and is compared with the conventional voltage-in and current-out (VICO) model. Experimental tests are conducted on a physical machine with different fault percentages. Power-hardware-in-the-loop (PHIL) emulation uses a power electronics converter to mimic the behavior of the stator inter-turn fault of the induction motor with real-time full power flow. A real-time controller is used for the controller development. The developed models are tested using a power- hardware-in-the-loop (PHIL) test setup. Calculation of back-emf of VBR model to compensate for the changes in filter values is discussed. The experimental emulation results are validated with the results from the physical machine to verify the emulation accuracy. The limitations and application of the work are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".