Evaluation of dynamic parameters and performance of deep-bar induction machines
Bibliographic record
Abstract
Deep rotor bar induction machines have good starting characteristics as compared with cylindrical bar squirrel-cage configurations. High starting torque is achieved by the speed-dependent secondary resistance. The variation of secondary resistance and leakage inductance determines the machine starting and acceleration performance. Conventional measurement methods can only provide nominal parameters under blocked rotor and no-load test conditions. This makes details of the dynamic parameters and an evaluation of the acceleration performance from standstill ill-defined. To determine the starting characteristics of deep bar induction machines, a knowledge of the dynamically varying parameters is needed. This report demonstrates the application of a new algorithm to identify the variable parameters of deep rotor bar induction machines, namely secondary resistance and secondary leakage inductance, based on the easily measurable terminal conditions of the machine, namely voltage, current and rotational speed. This algorithm is simply an algebraic operation, which is always stable and easily realizable and no convergence problems arise. Practical application of this method to an 18 pole, 60 Hz, 871 hp deep bar induction motor demonstrates the performance of the new parameter identification method.>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".