Development of interactive tutorial tool for simulation and identification of electrical machines and transformers
Bibliographic record
Abstract
The paper focuses on the development of a Matlab based interactive and tutorial tool for simulation and parameters identification of electrical machines, transformers and several other dynamic systems. The proposed software allows predicting the steady-state and dynamic performances of three-phase induction and synchronous machines, DC machines in both motor and generator modes, three-phase transformers and several other dynamic systems. A given machine under study is formatted in state space models. This allows performing various standard and non-standard tests. For linear and nonlinear deterministic machine models, linear and nonlinear deterministic predictors (Euler method and fourth order Runge-Kutta) are used, while the classical linear Kalman Filter (LKF) and Unscented Kalman Filter (UKF) are applied for the state estimation of linear and nonlinear stochastic machine models respectively. The availability of several optimization approaches for parameters identification experiences offers to users a great flexibility and opportunity to compare their robustness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.019 |
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".