Reconfigurable Star-Delta VBR Induction Machine Model for Predicting Soft-Starting Transients
Bibliographic record
Abstract
Induction machines are extensively utilized in many commercial and industrial applications. Simulations of such machines to analyze their starting and operational performance require numerically accurate and efficient models. The conventional qd models, although simple to implement, require snubber circuits for interfacing with an external network, which reduces the accuracy and makes the model computationally expensive. This paper extends the prior work and presents a reconfigurable star-delta constant-parameter voltage-behind-reactance (CPVBR) model of a three-phase squirrel-cage induction machine considering the low-frequency deep-rotor-bar phenomenon. The eigenvalue analysis and computer studies demonstrate that the proposed model yields superior computational performance while providing an efficient machine-network interface as compared to the established qd model. It is envisioned that the new model can be useful for efficient simulation of power systems including induction machines with star-delta starters.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".