Accelerating performance evaluation of deep-bar induction machines from parameter identification
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. High performance control of the machines requires a knowledge of real-time machine parameters as well. To determine the starting characteristics of deep bar induction machines, or to operate the machine at high performance, a knowledge of the dynamically-varying parameters is needed. This paper describes the application of a new algorithm identifying 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. The 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 evaluation of the starting and accelerating performance of a 4-pole, 60 Hz GE (General Electric, Canada) 10000 hp deep bar induction motor demonstrates the utility of the new parameter identification method.
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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 | 0.000 | 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 | 0.000 | 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".